
#for google colab
#from google.colab import drive
#drive.mount('/content/drive')


#import
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
import folium
import geopandas as gpd

#make sure the csv is in your top level drive directory, if you try and put it in a subfolder then that filepath becomes invalid for the others
data = pd.read_csv('Housing.csv')
#data = pd.read_csv('/content/drive/My Drive/Housing.csv')
#data = pd.read_csv('/content/drive/My Drive/cmsc320-finalproject/Housing.csv')


data = pd.read_csv('Housing.csv')
#parse


#handling NaNs
print(data.isnull().sum())

data['bedrooms'] = data['bedrooms'].fillna(data['bedrooms'].median()) # Filling missing values
data['bathrooms'] = data['bathrooms'].fillna(data['bathrooms'].median())

data.dropna(subset=['price'], inplace=True) # Dropping rows where the 'price' is missing

data['yr_renovated'] = data['yr_renovated'].fillna(0) # Filling 'yr_renovated' with 0 where NaNs may imply no renovation


#string converts
data['date'] = pd.to_datetime(data['date'])
data['id'] = data['id'].astype(str)


#encoding
data['house_age'] = data['date'].dt.year - data['yr_built']
#should make years since renovation equal years since it was built if 0
#data['years_since_renovation'] = data.apply(lambda row: row['date'].year - row['yr_renovated'] if row['yr_renovated'] != 0 else 0, axis=1)
data['years_since_renovation'] = data.apply(
    lambda row: row['date'].year - row['yr_renovated'] if row['yr_renovated'] != 0 else row['date'].year - row['yr_built'],
    axis=1
)
#new cols
data['num_rooms'] = data['bedrooms'] + data['bathrooms']
data['living percentage'] = (data['sqft_living'])/(data['sqft_lot'])
data['price_per_sqft_living'] = data['price']/data['sqft_living']
data['price_per_sqft_lot'] = data['price']/data['sqft_lot']

id               0
date             0
price            0
bedrooms         0
bathrooms        0
sqft_living      0
sqft_lot         0
floors           0
waterfront       0
view             0
condition        0
grade            0
sqft_above       0
sqft_basement    0
yr_built         0
yr_renovated     0
zipcode          0
lat              0
long             0
sqft_living15    0
sqft_lot15       0
dtype: int64


# organize (sort by date)
data.sort_values(by='date', inplace=True)
data.reset_index(drop=True, inplace=True)


#general testing
data.head(10)


#Mihir - Covariance matrix of all features
CovMatrix = data.cov()

#However correlation matrix will be more useful as it scales everything using standard deviations to between -1 to 1, makes it easier to compare between factors
CorrMatrix = data.corr(method = 'pearson')


#prints much better if you stick it in its own cell
data.cov()


data.corr(method = 'pearson')


#correlations of price with all other current variables
print(CorrMatrix['price'])

id                       -0.016762
date                     -0.004357
price                     1.000000
bedrooms                  0.308377
bathrooms                 0.525136
sqft_living               0.702035
sqft_lot                  0.089661
floors                    0.256793
waterfront                0.266370
view                      0.397294
condition                 0.036361
grade                     0.667434
sqft_above                0.605567
sqft_basement             0.323816
yr_built                  0.054011
yr_renovated              0.126434
zipcode                  -0.053201
lat                       0.307003
long                      0.021626
sqft_living15             0.585378
sqft_lot15                0.082447
house_age                -0.053950
years_since_renovation   -0.105671
num_rooms                 0.466325
living percentage         0.123115
price_per_sqft_living     0.554701
price_per_sqft_lot        0.307848
Name: price, dtype: float64


print(CorrMatrix['price_per_sqft_living'])

id                       -0.005526
date                      0.037970
price                     0.554701
bedrooms                 -0.206030
bathrooms                -0.091426
sqft_living              -0.092318
sqft_lot                 -0.033823
floors                    0.003837
waterfront                0.193215
view                      0.220932
condition                 0.102344
grade                     0.122757
sqft_above               -0.088620
sqft_basement            -0.025764
yr_built                 -0.289869
yr_renovated              0.105482
zipcode                   0.172637
lat                       0.472049
long                     -0.236033
sqft_living15             0.038560
sqft_lot15               -0.057516
house_age                 0.290573
years_since_renovation    0.249878
num_rooms                -0.176774
living percentage         0.116634
price_per_sqft_living     1.000000
price_per_sqft_lot        0.450463
Name: price_per_sqft_living, dtype: float64


#finding all other correlations that may be of interest
correlationThreshold = 0.3
checkedList = []

for row in CorrMatrix.index:
  for col in CorrMatrix.columns:
    if (not row in checkedList) and (not col in checkedList) and (row != col) and (CorrMatrix[row][col] > correlationThreshold or CorrMatrix[row][col] < -correlationThreshold):
      print(row, col, CorrMatrix[row][col])
  checkedList.append(row)

price bedrooms 0.3083769180156153
price bathrooms 0.5251363218554719
price sqft_living 0.7020346040056666
price view 0.39729352797680806
price grade 0.6674342691668146
price sqft_above 0.6055670405615354
price sqft_basement 0.3238155679576574
price lat 0.3070033728790943
price sqft_living15 0.5853783781780082
price num_rooms 0.4663250127585262
price price_per_sqft_living 0.5547008682090738
price price_per_sqft_lot 0.30784846349389644
bedrooms bathrooms 0.5159316152210395
bedrooms sqft_living 0.57669257587632
bedrooms grade 0.3569778987944419
bedrooms sqft_above 0.47761446504670363
bedrooms sqft_basement 0.30311202501453155
bedrooms sqft_living15 0.39166621103010474
bedrooms num_rooms 0.8954995511941201
bathrooms sqft_living 0.7546652789673763
bathrooms floors 0.5006531725878747
bathrooms grade 0.664982533878076
bathrooms sqft_above 0.6853424758761565
bathrooms yr_built 0.5060194382852586
bathrooms sqft_living15 0.5686342895782276
bathrooms house_age -0.5064069441397088
bathrooms years_since_renovation -0.537469341028698
bathrooms num_rooms 0.8432702460643129
sqft_living floors 0.35394929023671473
sqft_living grade 0.7627044764584723
sqft_living sqft_above 0.8765965986813177
sqft_living sqft_basement 0.4350429736698242
sqft_living yr_built 0.3180487689964427
sqft_living sqft_living15 0.7564202590172292
sqft_living house_age -0.31848847620130405
sqft_living years_since_renovation -0.3441348746046749
sqft_living num_rooms 0.7539307566069245
sqft_lot sqft_lot15 0.7185567524330329
floors grade 0.45818251367194757
floors sqft_above 0.5238847102851497
floors yr_built 0.48931942474365014
floors house_age -0.48963996546979405
floors years_since_renovation -0.505650922321103
floors num_rooms 0.37021041036533103
floors living percentage 0.5570303807316453
floors price_per_sqft_lot 0.4781204780795243
waterfront view 0.4018573506975677
condition yr_built -0.36141656224866653
condition house_age 0.3606652284795277
condition years_since_renovation 0.39552514408825024
grade sqft_above 0.7559229376236478
grade yr_built 0.4469632049266092
grade sqft_living15 0.7132020930151758
grade house_age -0.4474152409756253
grade years_since_renovation -0.46118017003453
grade num_rooms 0.5694767212967314
sqft_above yr_built 0.423898351663744
sqft_above long 0.3438030174605131
sqft_above sqft_living15 0.7318702923539874
sqft_above house_age -0.4242475329942809
sqft_above years_since_renovation -0.4361880253823974
sqft_above num_rooms 0.6557477942021749
sqft_basement num_rooms 0.337619598952765
yr_built zipcode -0.3468691778552612
yr_built long 0.4093562026388897
yr_built sqft_living15 0.3262288995957142
yr_built house_age -0.9998732929914715
yr_built years_since_renovation -0.9096524794041762
yr_built num_rooms 0.3596537193321721
zipcode long -0.564071606442267
zipcode house_age 0.34686352751305144
zipcode years_since_renovation 0.32056344232979844
lat price_per_sqft_living 0.47204862811027676
long sqft_living15 0.33460498382715503
long house_age -0.4093228953977021
long years_since_renovation -0.3828719068575162
sqft_living15 house_age -0.3265517538903627
sqft_living15 years_since_renovation -0.32485560112321155
sqft_living15 num_rooms 0.5411840225422982
house_age years_since_renovation 0.9099482701339621
house_age num_rooms -0.3599461100668001
years_since_renovation num_rooms -0.3831789177472444
living percentage price_per_sqft_lot 0.89620946404785
price_per_sqft_living price_per_sqft_lot 0.4504632365624928


houses_map = folium.Map(location=[data['lat'].mean(), data['long'].mean()], zoom_start=10)

for _, row in data.iterrows():
    folium.Marker(
        [row['lat'], row['long']]
    ).add_to(houses_map)

houses_map.save('AllHouseLocations.html')

from folium.plugins import HeatMap
houses_heatmap = folium.Map(location=[data['lat'].mean(), data['long'].mean()], zoom_start=10)
heat_data = data[['lat', 'long']].values.tolist()
HeatMap(heat_data).add_to(houses_heatmap)
houses_heatmap.save('AllHouseLocationsHeatmap.html')

houses_heatmap


# Analysis on boolean/categorical data (waterfront, view, condition, grade, floors)
def calculate_summary_statistics(data, column_name):
    count = data[column_name].count()  # Count non-null entries
    mean = data[column_name].mean()  # Mean
    std = data[column_name].std()  # Standard deviation
    min_val = data[column_name].min()  # Minimum value
    q25 = data[column_name].quantile(0.25)  # 25th percentile
    median = data[column_name].median()  # Median
    q75 = data[column_name].quantile(0.75)  # 75th percentile
    max_val = data[column_name].max()  # Maximum value

    return {
        "Count": count,
        "Mean": mean,
        "Standard Deviation": std,
        "Min": min_val,
        "25th Percentile": q25,
        "50th Percentile (Median)": median,
        "75th Percentile": q75,
        "Max": max_val
    }

columns_to_analyze = ['waterfront', 'view', 'condition', 'grade', 'floors']

# Calculating and displaying summary statistics for each column
summary_statistics = {}
for column in columns_to_analyze:
    summary_statistics[column] = calculate_summary_statistics(data, column)

summary_statistics

{'waterfront': {'Count': 21613,
  'Mean': 0.007541757275713691,
  'Standard Deviation': 0.08651719772790183,
  'Min': 0,
  '25th Percentile': 0.0,
  '50th Percentile (Median)': 0.0,
  '75th Percentile': 0.0,
  'Max': 1},
 'view': {'Count': 21613,
  'Mean': 0.23430342849211122,
  'Standard Deviation': 0.7663175692736391,
  'Min': 0,
  '25th Percentile': 0.0,
  '50th Percentile (Median)': 0.0,
  '75th Percentile': 0.0,
  'Max': 4},
 'condition': {'Count': 21613,
  'Mean': 3.4094295100171195,
  'Standard Deviation': 0.6507430463662562,
  'Min': 1,
  '25th Percentile': 3.0,
  '50th Percentile (Median)': 3.0,
  '75th Percentile': 4.0,
  'Max': 5},
 'grade': {'Count': 21613,
  'Mean': 7.656873178179799,
  'Standard Deviation': 1.1754587569743047,
  'Min': 1,
  '25th Percentile': 7.0,
  '50th Percentile (Median)': 7.0,
  '75th Percentile': 8.0,
  'Max': 13},
 'floors': {'Count': 21613,
  'Mean': 1.4943089807060566,
  'Standard Deviation': 0.5399888951423845,
  'Min': 1.0,
  '25th Percentile': 1.0,
  '50th Percentile (Median)': 1.5,
  '75th Percentile': 2.0,
  'Max': 3.5}}


# Chi-Square Test to waterfront and view, waterfront and condition, view and condition, condition and grade, grade and floors

from scipy.stats import chi2_contingency

def chi_square_test(data, var1, var2):
    contingency_table = pd.crosstab(data[var1], data[var2])
    chi2, p, dof, expected = chi2_contingency(contingency_table)
    return chi2, p, dof, expected

# Pairs of variables to test
variable_pairs = [
    ('waterfront', 'view'),
    ('waterfront', 'condition'),
    ('view', 'condition'),
    ('condition', 'grade'),
    ('grade', 'floors')
]

# Performing Chi-Square Tests
chi_square_results = {}
for var1, var2 in variable_pairs:
    chi2, p, dof, expected = chi_square_test(data, var1, var2)
    chi_square_results[(var1, var2)] = {'chi2': chi2, 'p-value': p, 'dof': dof, 'expected': expected}

chi_square_results


# Interpretation:
# Waterfront vs. View: The p-value is 0.0, which is less than 0.05, indicating that there is a significant association between the waterfront status and the view rating.
# Waterfront vs. Condition: The p-value is 0.039, which is less than 0.05, indicating that there is a significant association between the waterfront status and the condition of the house.
# View vs. Condition: The p-value is extremely low (1.82e-08), indicating a significant association between the view rating and the condition of the house.
# Condition vs. Grade: The p-value is 0.0, which is less than 0.05, indicating a significant association between the condition of the house and its grade.
# Grade vs. Floors: The p-value is 0.0, which is less than 0.05, indicating a significant association between the grade of the house and the number of floors.

{('waterfront', 'view'): {'chi2': 7572.5563318397735,
  'p-value': 0.0,
  'dof': 4,
  'expected': array([[1.93420187e+04, 3.29496137e+02, 9.55737288e+02, 5.06153704e+02,
          3.16594179e+02],
         [1.46981308e+02, 2.50386342e+00, 7.26271226e+00, 3.84629621e+00,
          2.40582057e+00]])},
 ('waterfront', 'condition'): {'chi2': 10.074729585287205,
  'p-value': 0.03918751144589182,
  'dof': 4,
  'expected': array([[2.97737473e+01, 1.70702818e+02, 1.39251816e+04, 5.63617036e+03,
          1.68817147e+03],
         [2.26252718e-01, 1.29718225e+00, 1.05818396e+02, 4.28296396e+01,
          1.28285291e+01]])},
 ('view', 'condition'): {'chi2': 68.50224408217254,
  'p-value': 1.822893843270391e-08,
  'dof': 16,
  'expected': array([[2.70517744e+01, 1.55096840e+02, 1.26521149e+04, 5.12090089e+03,
          1.53383561e+03],
         [4.60833757e-01, 2.64211354e+00, 2.15531948e+02, 8.72358303e+01,
          2.61292740e+01],
         [1.33669551e+00, 7.66372091e+00, 6.25172489e+02, 2.53036460e+02,
          7.57906353e+01],
         [7.07907278e-01, 4.05866839e+00, 3.31088234e+02, 1.34006848e+02,
          4.01383427e+01],
         [4.42789062e-01, 2.53865729e+00, 2.07092444e+02, 8.38199695e+01,
          2.51061398e+01]])},
 ('condition', 'grade'): {'chi2': 2225.6248376517715,
  'p-value': 0.0,
  'dof': 44,
  'expected': array([[1.38805349e-03, 4.16416046e-03, 4.02535511e-02, 3.35908944e-01,
          2.82885301e+00, 1.24661084e+01, 8.42270856e+00, 3.62975987e+00,
          1.57405265e+00, 5.53833341e-01, 1.24924814e-01, 1.80446953e-02],
         [7.95817332e-03, 2.38745200e-02, 2.30787026e-01, 1.92587794e+00,
          1.62187572e+01, 7.14723546e+01, 4.82901957e+01, 2.08106232e+01,
          9.02456855e+00, 3.17531116e+00, 7.16235599e-01, 1.03456253e-01],
         [6.49192616e-01, 1.94757785e+00, 1.88265859e+01, 1.57104613e+02,
          1.32305455e+03, 5.83039888e+03, 3.93930079e+03, 1.69763869e+03,
          7.36184426e+02, 2.59027854e+02, 5.84273354e+01, 8.43950400e+00],
         [2.62758525e-01, 7.88275575e-01, 7.61999722e+00, 6.35875630e+01,
          5.35501874e+02, 2.35983431e+03, 1.59441873e+03, 6.87113543e+02,
          2.97968167e+02, 1.04840651e+02, 2.36482672e+01, 3.41586082e+00],
         [7.87026327e-02, 2.36107898e-01, 2.28237635e+00, 1.90460371e+01,
          1.60395965e+02, 7.06828344e+02, 4.77567575e+02, 2.05807384e+02,
          8.92487855e+01, 3.14023504e+01, 7.08323694e+00, 1.02313422e+00]])},
 ('grade', 'floors'): {'chi2': 6505.2900602236405,
  'p-value': 0.0,
  'dof': 55,
  'expected': array([[4.94147041e-01, 8.83727386e-02, 3.81298293e-01, 7.44922038e-03,
          2.83625596e-02, 3.70147596e-04],
         [1.48244112e+00, 2.65118216e-01, 1.14389488e+00, 2.23476611e-02,
          8.50876787e-02, 1.11044279e-03],
         [1.43302642e+01, 2.56280942e+00, 1.10576505e+01, 2.16027391e-01,
          8.22514228e-01, 1.07342803e-02],
         [1.19583584e+02, 2.13862027e+01, 9.22741868e+01, 1.80271133e+00,
          6.86373942e+00, 8.95757183e-02],
         [1.00707167e+03, 1.80103641e+02, 7.77085921e+02, 1.51815111e+01,
          5.78028964e+01, 7.54360801e-01],
         [4.43793458e+03, 7.93675566e+02, 3.42443997e+03, 6.69014482e+01,
          2.54724148e+02, 3.32429556e+00],
         [2.99848425e+03, 5.36245778e+02, 2.31371804e+03, 4.52018692e+01,
          1.72104011e+02, 2.24605561e+00],
         [1.29219451e+03, 2.31094712e+02, 9.97095035e+02, 1.94797113e+01,
          7.41680933e+01, 9.67935964e-01],
         [5.60362745e+02, 1.00214686e+02, 4.32392264e+02, 8.44741591e+00,
          3.21631426e+01, 4.19747374e-01],
         [1.97164669e+02, 3.52607227e+01, 1.52138019e+02, 2.97223893e+00,
          1.13166613e+01, 1.47688891e-01],
         [4.44732337e+01, 7.95354648e+00, 3.43168463e+01, 6.70429834e-01,
          2.55263036e+00, 3.33132837e-02],
         [6.42391153e+00, 1.14884560e+00, 4.95687781e+00, 9.68398649e-02,
          3.68713274e-01, 4.81191875e-03]])}}


# Susie - Test if the mean price of houses with a basement is significantly different from those without a basement
from scipy.stats import ttest_ind

# Split data based on houses with/without basement
with_basement = data[data['sqft_basement'] > 0]
without_basement = data[data['sqft_basement'] == 0]

# Performing a t-test
t_stat, p_val = ttest_ind(with_basement['price'], without_basement['price'])

print(f"T-statistic: {t_stat}, P-value: {p_val}")

# Visualization
plt.figure(figsize=(10, 6))
sns.boxplot(x='sqft_basement', y='price', data=data.assign(sqft_basement=data['sqft_basement']>0))
plt.title('Price Distribution With vs. Without Basement')
plt.xlabel('Has Basement')
plt.ylabel('Price')
plt.show()

T-statistic: 26.93602313477148, P-value: 3.266907478647668e-157


zipcode_df = data.groupby('zipcode').agg(
    avg_price=('price', 'mean'),
    avg_price_per_sqft=('price_per_sqft_living', 'mean'),
    avg_sqft_living = ('sqft_living', 'mean'),
    avg_sqft_lot = ('sqft_lot', 'mean'),
    avg_age = ('house_age','mean'),
    total_houses=('zipcode', 'size')
).reset_index()
zipcode_df


plt.figure(figsize=(14, 8))
sns.boxplot(x='zipcode', y='price', data=data)
plt.xticks(rotation=90)
plt.title('Price Distribution by Zip Code')
plt.xlabel('Zip Code')
plt.ylabel('Price')
plt.show()


zipcode_df["house_age"] = data.groupby("zipcode").apply(lambda x: x["house_age"].mean()).sort_values(ascending=False)
zipcode_df.corr(method="pearson")


data["zipcode"].unique()

array([98027, 98117, 98029, 98065, 98006, 98106, 98011, 98052, 98133,
       98102, 98001, 98092, 98125, 98059, 98199, 98115, 98107, 98024,
       98155, 98072, 98042, 98116, 98034, 98119, 98077, 98045, 98105,
       98007, 98074, 98166, 98008, 98198, 98003, 98014, 98136, 98023,
       98033, 98038, 98103, 98055, 98075, 98058, 98122, 98053, 98118,
       98112, 98004, 98177, 98019, 98144, 98056, 98005, 98168, 98146,
       98028, 98108, 98040, 98148, 98010, 98030, 98178, 98032, 98109,
       98126, 98031, 98070, 98022, 98188, 98002, 98039])


#data scraping

from bs4 import BeautifulSoup
import csv
import pandas as pd
import requests
import re

#needed to avoid 403 when scraping
headers = {
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
    'Accept-Encoding': 'gzip, deflate, br, zstd',
    'Accept-Language': 'en-US,en;q=0.9',
    'Cache-Control': 'no-cache',
    'Content-Type': 'application/x-www-form-urlencoded',
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#much of the data is in tables;
#tags_of_interest contains the labeled data we care about
#when the tuple has two elements the first is the tag and the second is the label
#when the tuple has three elements, the third is the key for the dictionary
tags_of_interest = [("th", "Population"), ("th", "Population Density"), ("th", "Housing Units"), ("th", "Median Home Value"), ("th", "Land Area"), ("th", "Water Area"), ("th", "Occupied Housing Units"), ("th", "Median Household Income"),
                    ("td", "Median Age: ", "Median Age"), ("td", "Male Median Age: ", "Male Median Age"), ("td", "Female Median Age: ", "Female Median Age"), ("th", 'Male', "Male Population"), ("th", "Female", "Female Population"), ("th", "White", "White Population"), ("th", "Black", "Black or African American Population"), ("th", "American Indian", "American Indian or Alaskan Native Population"), ("th", "Asian", "Asian Population"), ("th", "Hawaiian", "Native Hawaiian and Other Pacific Islander Population"), ("th", "Other Race", "Other Race Population"), ("th", "Two Or More Races", "Two or More Races Population"), ("th", "Husband Wife", "Husband and Wife Family Households"), ("th", "Single Guardian", "Single Guardian Households"), ("th", "Singles", "Singles Households"), ("th", "Singles With Roommate", "Singles with Roommate Households"), ("th", "Households without Kids"), ("th", "Households with Kids"), ("th", "In Occupied Housing Units"), ("th", "Correctional Facility", "Correctional Facilities"), ("th", "Juvenile Facilities"), ("th", "Nursing Facilities"), ("th", "Other Institutional"), ("th", "College Student Housing"), ("th", "Military Quarters"), ("th", "Other Noninstitutional"), ("th", "Owned Households With A Mortgage", "Owned House with Mortgage"), ("th", "Owned Households Free", "Owned House Entirely"), ("th", "Renter Occupied Households", "House Occupied by Renter"), ("th", "Households Vacant"), ("th", "Studio Apartment", "Rented Studio Apartment"), ("th", "1 Bedroom", "Rented 1 Bedroom"), ("th", "2 Bedroom", "Rented 2 Bedroom"), ("th", "3+ Bedroom", "Rented 3+ Bedroom"), ("th", "Worked Full-time"), ("th", "Worked Part-time"), ("th", "No Earnings"), ("th", "Car, truck, or van", "Commute in Car, Truck, or Van"), ("th", "Public transportation", "Commute in Public Transportation"), ("th", "Taxicab", "Commute in Taxicab"), ("th", "Motorcycle", "Commute in Motorcycle"), ("th", "Bicycle", "Commute in Bicycle, Walking, Other"), ("th", "Worked at Home"), ("th", "Less than High School Diploma"), ("th", "High School Graduate"), ("th", "Associate's degree"), ("th", "Master's degree"), ("th", "Professional school degree"), ("th", "Doctorate degree"), ("th", "Enrolled in Public School"), ("th", "Enrolled in Private School"), ("th", "Not Enrolled in School")]

#get_sibling_after gets the value of the sibling after the tag with label
def get_sibling_after(soup, tag, label):
    #find_contains returns true if tag.text contains label
    def find_contains(value):
        return value.name == tag and label in value.text.replace('\xa0', '')
    return soup.find(find_contains).find_next_sibling().text



zipcodes = [98027, 98117, 98029, 98065, 98006, 98106, 98011, 98052, 98133,
       98102, 98001, 98092, 98125, 98059, 98199, 98115, 98107, 98024,
       98155, 98072, 98042, 98116, 98034, 98119, 98077, 98045, 98105,
       98007, 98074, 98166, 98008, 98198, 98003, 98014, 98136, 98023,
       98033, 98038, 98103, 98055, 98075, 98058, 98122, 98053, 98118,
       98112, 98004, 98177, 98019, 98144, 98056, 98005, 98168, 98146,
       98028, 98108, 98040, 98148, 98010, 98030, 98178, 98032, 98109,
       98126, 98031, 98070, 98022, 98188, 98002, 98039]

zipcode_data = []
for zipcode in zipcodes:
    url = f"https://www.unitedstateszipcodes.org/{zipcode}/"
    print(f"Now scraping: {zipcode}\nURL: {url}\n")
    page = requests.get(url, headers=headers)
    soup = BeautifulSoup(page.content, "html.parser")
    zdata = {"zipcode": zipcode}
    for tag in tags_of_interest:
        key = tag[2] if len(tag) == 3 else tag[1]
        zdata[key] = get_sibling_after(soup, tag[0], tag[1])
    zipcode_data.append(zdata)


#print(zipcode_data)


scraped_df = pd.DataFrame(zipcode_data).set_index('zipcode')
print(scraped_df.head())
scraped_df.to_csv("zipcode_data.csv")

Now scraping: 98027
URL: https://www.unitedstateszipcodes.org/98027/

Now scraping: 98117
URL: https://www.unitedstateszipcodes.org/98117/

Now scraping: 98029
URL: https://www.unitedstateszipcodes.org/98029/

Now scraping: 98065
URL: https://www.unitedstateszipcodes.org/98065/

Now scraping: 98006
URL: https://www.unitedstateszipcodes.org/98006/

Now scraping: 98106
URL: https://www.unitedstateszipcodes.org/98106/

Now scraping: 98011
URL: https://www.unitedstateszipcodes.org/98011/

Now scraping: 98052
URL: https://www.unitedstateszipcodes.org/98052/

Now scraping: 98133
URL: https://www.unitedstateszipcodes.org/98133/

Now scraping: 98102
URL: https://www.unitedstateszipcodes.org/98102/

Now scraping: 98001
URL: https://www.unitedstateszipcodes.org/98001/

Now scraping: 98092
URL: https://www.unitedstateszipcodes.org/98092/

Now scraping: 98125
URL: https://www.unitedstateszipcodes.org/98125/

Now scraping: 98059
URL: https://www.unitedstateszipcodes.org/98059/

Now scraping: 98199
URL: https://www.unitedstateszipcodes.org/98199/

Now scraping: 98115
URL: https://www.unitedstateszipcodes.org/98115/

Now scraping: 98107
URL: https://www.unitedstateszipcodes.org/98107/

Now scraping: 98024
URL: https://www.unitedstateszipcodes.org/98024/

Now scraping: 98155
URL: https://www.unitedstateszipcodes.org/98155/

Now scraping: 98072
URL: https://www.unitedstateszipcodes.org/98072/

Now scraping: 98042
URL: https://www.unitedstateszipcodes.org/98042/

Now scraping: 98116
URL: https://www.unitedstateszipcodes.org/98116/

Now scraping: 98034
URL: https://www.unitedstateszipcodes.org/98034/

Now scraping: 98119
URL: https://www.unitedstateszipcodes.org/98119/

Now scraping: 98077
URL: https://www.unitedstateszipcodes.org/98077/

Now scraping: 98045
URL: https://www.unitedstateszipcodes.org/98045/

Now scraping: 98105
URL: https://www.unitedstateszipcodes.org/98105/

Now scraping: 98007
URL: https://www.unitedstateszipcodes.org/98007/

Now scraping: 98074
URL: https://www.unitedstateszipcodes.org/98074/

Now scraping: 98166
URL: https://www.unitedstateszipcodes.org/98166/

Now scraping: 98008
URL: https://www.unitedstateszipcodes.org/98008/

Now scraping: 98198
URL: https://www.unitedstateszipcodes.org/98198/

Now scraping: 98003
URL: https://www.unitedstateszipcodes.org/98003/

Now scraping: 98014
URL: https://www.unitedstateszipcodes.org/98014/

Now scraping: 98136
URL: https://www.unitedstateszipcodes.org/98136/

Now scraping: 98023
URL: https://www.unitedstateszipcodes.org/98023/

Now scraping: 98033
URL: https://www.unitedstateszipcodes.org/98033/

Now scraping: 98038
URL: https://www.unitedstateszipcodes.org/98038/

Now scraping: 98103
URL: https://www.unitedstateszipcodes.org/98103/

Now scraping: 98055
URL: https://www.unitedstateszipcodes.org/98055/

Now scraping: 98075
URL: https://www.unitedstateszipcodes.org/98075/

Now scraping: 98058
URL: https://www.unitedstateszipcodes.org/98058/

Now scraping: 98122
URL: https://www.unitedstateszipcodes.org/98122/

Now scraping: 98053
URL: https://www.unitedstateszipcodes.org/98053/

Now scraping: 98118
URL: https://www.unitedstateszipcodes.org/98118/

Now scraping: 98112
URL: https://www.unitedstateszipcodes.org/98112/

Now scraping: 98004
URL: https://www.unitedstateszipcodes.org/98004/

Now scraping: 98177
URL: https://www.unitedstateszipcodes.org/98177/

Now scraping: 98019
URL: https://www.unitedstateszipcodes.org/98019/

Now scraping: 98144
URL: https://www.unitedstateszipcodes.org/98144/

Now scraping: 98056
URL: https://www.unitedstateszipcodes.org/98056/

Now scraping: 98005
URL: https://www.unitedstateszipcodes.org/98005/

Now scraping: 98168
URL: https://www.unitedstateszipcodes.org/98168/

Now scraping: 98146
URL: https://www.unitedstateszipcodes.org/98146/

Now scraping: 98028
URL: https://www.unitedstateszipcodes.org/98028/

Now scraping: 98108
URL: https://www.unitedstateszipcodes.org/98108/

Now scraping: 98040
URL: https://www.unitedstateszipcodes.org/98040/

Now scraping: 98148
URL: https://www.unitedstateszipcodes.org/98148/

Now scraping: 98010
URL: https://www.unitedstateszipcodes.org/98010/

Now scraping: 98030
URL: https://www.unitedstateszipcodes.org/98030/

Now scraping: 98178
URL: https://www.unitedstateszipcodes.org/98178/

Now scraping: 98032
URL: https://www.unitedstateszipcodes.org/98032/

Now scraping: 98109
URL: https://www.unitedstateszipcodes.org/98109/

Now scraping: 98126
URL: https://www.unitedstateszipcodes.org/98126/

Now scraping: 98031
URL: https://www.unitedstateszipcodes.org/98031/

Now scraping: 98070
URL: https://www.unitedstateszipcodes.org/98070/

Now scraping: 98022
URL: https://www.unitedstateszipcodes.org/98022/

Now scraping: 98188
URL: https://www.unitedstateszipcodes.org/98188/

Now scraping: 98002
URL: https://www.unitedstateszipcodes.org/98002/

Now scraping: 98039
URL: https://www.unitedstateszipcodes.org/98039/

        Population Population Density Housing Units Median Home Value  \
zipcode                                                                 
98027       26,141                469        11,248          $478,800   
98117       31,365              7,953        14,213          $463,500   
98029       24,348              2,719        10,222          $446,900   
98065       12,699                171         4,556          $418,900   
98006       36,364              3,402        13,891          $574,000   

        Land Area Water Area Occupied Housing Units Median Household Income  \
zipcode                                                                       
98027       55.79       1.13                 10,454                $100,644   
98117        3.94       0.61                 13,667                 $86,986   
98029        8.95       0.02                  9,656                 $99,974   
98065       74.09       0.87                  4,278                $121,415   
98006       10.69       0.75                 13,288                $110,290   

        Median Age Male Median Age  ... Worked at Home  \
zipcode                             ...                  
98027           41              40  ...            900   
98117           40              39  ...          1,363   
98029           35              34  ...            841   
98065           34              34  ...            451   
98006           42              41  ...          1,368   

        Less than High School Diploma High School Graduate Associate's degree  \
zipcode                                                                         
98027                             703                6,547              1,545   
98117                             646                5,998              1,627   
98029                             400                4,577              1,546   
98065                             234                2,747                655   
98006                             559                6,238              1,955   

        Master's degree Professional school degree Doctorate degree  \
zipcode                                                               
98027             3,365                        684              314   
98117             4,072                      1,290              606   
98029             2,925                        593              690   
98065             1,063                        313               90   
98006             4,184                      1,361            1,137   

        Enrolled in Public School Enrolled in Private School  \
zipcode                                                        
98027                       3,680                        711   
98117                       2,831                      1,458   
98029                       4,359                        965   
98065                       2,397                        908   
98006                       6,315                      1,363   

        Not Enrolled in School  
zipcode                         
98027                      286  
98117                      292  
98029                      556  
98065                      213  
98006                      422  

[5 rows x 60 columns]


#comment out if csv doesn't exist yet
scraped_df = pd.read_csv('zipcode_data.csv')


scraped_zipcode_df = pd.merge(zipcode_df, scraped_df, on = 'zipcode', how='left').reset_index()
scraped_zipcode_df.head()


zipcodes_geojson = 'washington_zipcodes.geojson'
gdf = gpd.read_file(zipcodes_geojson)


import folium

gdf.to_csv('gdf.csv')

scraped_zipcode_df['Population'] = scraped_zipcode_df['Population'].str.replace(',', '').astype(int)
for column in scraped_zipcode_df.columns:
    if scraped_zipcode_df[column].dtype != 'object':
        scraped_zipcode_df[column].fillna(scraped_zipcode_df[column].mean(),inplace=True)
scraped_zipcode_df = scraped_zipcode_df.apply(pd.to_numeric, errors='coerce')
scraped_zipcode_df.dropna(axis=1, inplace=True)

html_files = []
for column in scraped_zipcode_df.columns:
    if (column != 'zipcode' and column != 'index'):
        print(column)
        column_map = folium.Map(location=[47.6061, -122.3328], zoom_start=10)
        folium.Choropleth(
            geo_data=gdf,
            name=f'choropleth_{column}',
            data=scraped_zipcode_df,
            columns=['zipcode', column],
            key_on='feature.properties.ZCTA5CE10',
            fill_color='YlOrRd',
            fill_opacity=0.7,
            line_opacity=0.2,
            legend_name=f'{column}'
        ).add_to(column_map)

        file_name = f'choropleth_{column}.html'
        html_files.append(file_name)
        column_map.save(file_name)
        print(f'Saved {file_name}')
        column_map

avg_price
Saved choropleth_avg_price.html
avg_price_per_sqft
Saved choropleth_avg_price_per_sqft.html
avg_sqft_living
Saved choropleth_avg_sqft_living.html
avg_sqft_lot
Saved choropleth_avg_sqft_lot.html
avg_age
Saved choropleth_avg_age.html
total_houses
Saved choropleth_total_houses.html
Population
Saved choropleth_Population.html
Land Area
Saved choropleth_Land Area.html
Water Area
Saved choropleth_Water Area.html
Median Age
Saved choropleth_Median Age.html
Male Median Age
Saved choropleth_Male Median Age.html
Female Median Age
Saved choropleth_Female Median Age.html
Correctional Facilities
Saved choropleth_Correctional Facilities.html
Juvenile Facilities
Saved choropleth_Juvenile Facilities.html
Nursing Facilities
Saved choropleth_Nursing Facilities.html
Other Institutional
Saved choropleth_Other Institutional.html
Military Quarters
Saved choropleth_Military Quarters.html
Other Noninstitutional
Saved choropleth_Other Noninstitutional.html
Commute in Taxicab
Saved choropleth_Commute in Taxicab.html
Commute in Motorcycle
Saved choropleth_Commute in Motorcycle.html


from IPython.display import IFrame

for html_file in html_files:
    print(html_file)
    display(IFrame(html_file, width=700, height=500))

choropleth_avg_price.html

choropleth_avg_price_per_sqft.html

choropleth_avg_sqft_living.html

choropleth_avg_sqft_lot.html

choropleth_avg_age.html

choropleth_total_houses.html

choropleth_Population.html

choropleth_Land Area.html

choropleth_Water Area.html

choropleth_Median Age.html


groups = [group['price'].values for name, group in data.groupby('zipcode')]
f_statistic, p_value = stats.f_oneway(*groups)
print(f"F-statistic: {f_statistic}, p-value: {p_value}")

F-statistic: 214.6322029409192, p-value: 0.0


groups = [group['price_per_sqft_living'].values for name, group in data.groupby('zipcode')]
f_statistic, p_value = stats.f_oneway(*groups)
print(f"F-statistic: {f_statistic}, p-value: {p_value}")

F-statistic: 385.57121676127895, p-value: 0.0


from statsmodels.stats.multicomp import pairwise_tukeyhsd

data_flat = np.concatenate(groups)
labels = []
for i, group in enumerate(groups):
    labels.extend([f"Group{i+1}"] * len(group))
tukey_results = pairwise_tukeyhsd(data_flat, labels)
print(tukey_results)

/home/mihir/anaconda3/lib/python3.11/site-packages/scipy/integrate/_quadpack_py.py:1233: IntegrationWarning: The integral is probably divergent, or slowly convergent.
  quad_r = quad(f, low, high, args=args, full_output=self.full_output,

    Multiple Comparison of Means - Tukey HSD, FWER=0.05    
===========================================================
 group1  group2  meandiff p-adj    lower     upper   reject
-----------------------------------------------------------
 Group1 Group10   74.5993    0.0   47.4408  101.7578   True
 Group1 Group11   71.6966    0.0   39.8838  103.5094   True
 Group1 Group12   51.6138    0.0   24.2241   79.0035   True
 Group1 Group13   30.4185 0.0018    4.7731   56.0639   True
 Group1 Group14   -2.4681    1.0  -23.5761   18.6398  False
 Group1 Group15  103.8457    0.0   66.2659  141.4255   True
 Group1 Group16  100.1844    0.0   78.1593  122.2094   True
 Group1 Group17   73.7551    0.0   49.4957   98.0146   True
 Group1 Group18  120.6829    0.0   97.2432  144.1227   True
 Group1 Group19    3.7683    1.0  -21.1989   28.7354  False
 Group1  Group2   -0.2138    1.0  -27.1943   26.7666  False
 Group1 Group20    9.6525    1.0  -14.8296   34.1345  False
 Group1 Group21    2.8316    1.0  -28.8863   34.5495  False
 Group1 Group22  191.7745    0.0  169.9892  213.5598   True
 Group1 Group23  114.5643    0.0   93.8343  135.2944   True
 Group1 Group24   22.2683 0.0111    1.8562   42.6804   True
 Group1 Group25  416.6954    0.0  370.5681  462.8228   True
 Group1 Group26  235.9046    0.0   211.621  260.1882   True
 Group1 Group27   12.9605 0.9736   -7.7469   33.6679  False
 Group1 Group28   69.0766    0.0    42.977   95.1761   True
 Group1 Group29  128.9984    0.0  108.4784  149.5184   True
 Group1  Group3    5.7255    1.0  -18.6068   30.0578  False
 Group1 Group30  118.0839    0.0     95.97  140.1978   True
 Group1 Group31   29.0146 0.0022     4.377   53.6521   True
 Group1 Group32   64.1482    0.0   42.0472   86.2492   True
 Group1 Group33   26.8164 0.0005    5.2836   48.3492   True
 Group1 Group34   55.8544    0.0   34.4545   77.2543   True
 Group1 Group35   59.4981    0.0    35.839   83.1572   True
 Group1 Group36  129.9554    0.0   97.5458  162.3651   True
 Group1 Group37   96.1241    0.0   71.6165  120.6317   True
 Group1 Group38  114.2834    0.0   92.5997  135.9671   True
 Group1 Group39  117.2519    0.0   94.4792  140.0247   True
 Group1  Group4  324.0477    0.0  300.5297  347.5657   True
 Group1 Group40   92.8904    0.0    65.866  119.9148   True
 Group1 Group41    4.4263    1.0  -18.4764    27.329  False
 Group1 Group42  271.7846    0.0  237.8956  305.6736   True
 Group1 Group43  218.4531    0.0  198.1185  238.7877   True
 Group1 Group44  253.7664    0.0  227.9515  279.5814   True
 Group1 Group45    79.939    0.0   56.7603  103.1177   True
 Group1 Group46   231.564    0.0  206.8733  256.2548   True
 Group1 Group47   73.0553    0.0   45.4732  100.6375   True
 Group1 Group48  282.0133    0.0  248.6097  315.4168   True
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Group66  Group7   -2.8697    1.0  -34.9558   29.2164  False
Group66 Group70   83.6276    0.0    57.909  109.3462   True
Group66  Group8     8.799    1.0  -17.5994   35.1974  False
Group66  Group9  -82.8234    0.0 -118.8973  -46.7495   True
Group67 Group68  -20.1956 0.9743  -52.5081   12.1169  False
Group67 Group69  -10.7743    1.0   -37.054   15.5053  False
Group67  Group7  100.8461    0.0    68.913  132.7792   True
Group67 Group70  187.3434    0.0  161.8159  212.8709   True
Group67  Group8  112.5148    0.0   86.3026   138.727   True
Group67  Group9   20.8924 0.9941  -15.0455   56.8303  False
Group68 Group69    9.4213    1.0  -22.5343   41.3769  False
Group68  Group7  121.0417    0.0   84.2957  157.7877   True
Group68 Group70   207.539    0.0   176.199  238.8791   True
Group68  Group8  132.7104    0.0  100.8102  164.6106   True
Group68  Group9    41.088 0.0365     0.813   81.3631   True
Group69  Group7  111.6204    0.0   80.0484  143.1924   True
Group69 Group70  198.1177    0.0  173.0435   223.192   True
Group69  Group8  123.2891    0.0   97.5181  149.0601   True
Group69  Group9   31.6667 0.2203   -3.9506   67.2841  False
 Group7 Group70   86.4973    0.0   55.5486  117.4461   True
 Group7  Group8   11.6687    1.0  -19.8472   43.1846  False
 Group7  Group9  -79.9537    0.0  -119.925  -39.9824   True
Group70  Group8  -74.8286    0.0  -99.8322   -49.825   True
Group70  Group9  -166.451    0.0 -201.5171 -131.3849   True
 Group8  Group9  -91.6224    0.0   -127.19  -56.0547   True
-----------------------------------------------------------


data.insert(2, 'Season', "")
def seasonCalc(x):
  winter = [12, 1, 2]
  spring = [3, 4, 5]
  summer = [6, 7, 8]
  fall = [9, 10, 11]
  month = x.month
  if(month in winter):
    return "Winter"
  if(month in spring):
    return "Spring"
  if(month in summer):
    return "Summer"
  if(month in fall):
    return "Fall"

data['Season'] = data['date'].apply(lambda x: seasonCalc(x))
print(data['Season'].value_counts())

Season
Spring    6520
Summer    6331
Fall      5063
Winter    3699
Name: count, dtype: int64


observed_data = data['Season'].value_counts()
expected_data = pd.Series([len(data['Season'])/4] * 4)
_ , p_val = stats.chisquare(observed_data, expected_data)
print(p_val)

2.004746007881965e-205


plt.plot(data['date'], data['price'])
plt.title('Price of Houses over Time')
plt.xlabel('Date')
plt.ylabel('Price ($ Millions of USD)')

Text(0, 0.5, 'Price ($ Millions of USD)')


#Using complete original data
from sklearn.model_selection import train_test_split
Y = data['price']
X = data.drop(['id', 'date', 'price', 'price_per_sqft_living', 'price_per_sqft_lot'], axis=1)
random_state = 42

#As we can't take date we will instead one hot encode the seasons
X['Summer'] = X['Season'].apply(lambda x: 1 if x == 'Summer' else 0)
X['Fall'] = X['Season'].apply(lambda x: 1 if x == 'Fall' else 0)
X['Winter'] = X['Season'].apply(lambda x: 1 if x == 'Winter' else 0)
X['Spring'] = X['Season'].apply(lambda x: 1 if x == 'Spring' else 0)

X = X.drop(['Season'], axis=1)

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state = random_state, shuffle = True)

print(X_train.columns)

Index(['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',
       'waterfront', 'view', 'condition', 'grade', 'sqft_above',
       'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',
       'sqft_living15', 'sqft_lot15', 'house_age', 'years_since_renovation',
       'num_rooms', 'living percentage', 'Summer', 'Fall', 'Winter', 'Spring'],
      dtype='object')


import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.preprocessing import StandardScaler
import time

device = (
    "cuda"
    if torch.cuda.is_available()
    else "mps"
    if torch.backends.mps.is_available()
    else "cpu"
)

#Converting to numpy arrays
X_train = X_train.values
X_test = X_test.values
Y_train = Y_train.values
Y_test = Y_test.values

#Normalizing features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

#Converting to PyTorch tensors
X_train = torch.tensor(X_train, dtype=torch.float32).to(device)
X_test = torch.tensor(X_test, dtype=torch.float32).to(device)
Y_train = torch.tensor(Y_train, dtype=torch.float32).view(-1, 1).to(device)
Y_test = torch.tensor(Y_test, dtype=torch.float32).view(-1, 1).to(device)

start_time = time.time()

class HousePricePredictor(nn.Module):
    def __init__(self, input_size):
        super(HousePricePredictor, self).__init__()
        self.fc0 = nn.Linear(input_size, 512)
        self.fc1 = nn.Linear(512, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 64)
        self.fc4 = nn.Linear(64, 1)
        
        
    def forward(self, x):
        x = torch.relu(self.fc0(x))
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = torch.relu(self.fc3(x))
        x = self.fc4(x)
        return x

input_size = X_train.shape[1]
model = HousePricePredictor(input_size).to(device)

#training loop
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
train_losses = []
num_epochs = 10000
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    outputs = model(X_train)
    loss = criterion(outputs, Y_train)
    loss.backward()
    optimizer.step()
    train_losses.append(loss.item())
    
    if (epoch+1) % 10 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

#Evaluating the model
model.eval()
with torch.no_grad():
    predictions = model(X_test)
    test_loss = criterion(predictions, Y_test)
    print(f'Test Loss: {test_loss.item():.4f}')

end_time = time.time()
training_time = end_time - start_time
print(f"Training time: {training_time:.2f} seconds")

plt.plot(range(num_epochs), train_losses, label='Training Losses')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training Loss Curve')
plt.legend()
plt.show()

Epoch [10/10000], Loss: 433123917824.0000
Epoch [20/10000], Loss: 433086398464.0000
Epoch [30/10000], Loss: 432900997120.0000
Epoch [40/10000], Loss: 432219324416.0000
Epoch [50/10000], Loss: 430147371008.0000
Epoch [60/10000], Loss: 424738095104.0000
Epoch [70/10000], Loss: 412279668736.0000
Epoch [80/10000], Loss: 386660925440.0000
Epoch [90/10000], Loss: 339749470208.0000
Epoch [100/10000], Loss: 265112322048.0000
Epoch [110/10000], Loss: 169517957120.0000
Epoch [120/10000], Loss: 91106967552.0000
Epoch [130/10000], Loss: 72370700288.0000
Epoch [140/10000], Loss: 65880739840.0000
Epoch [150/10000], Loss: 58765479936.0000
Epoch [160/10000], Loss: 55995146240.0000
Epoch [170/10000], Loss: 53035900928.0000
Epoch [180/10000], Loss: 50626605056.0000
Epoch [190/10000], Loss: 48462565376.0000
Epoch [200/10000], Loss: 46523518976.0000
Epoch [210/10000], Loss: 44782870528.0000
Epoch [220/10000], Loss: 43214204928.0000
Epoch [230/10000], Loss: 41808957440.0000
Epoch [240/10000], Loss: 40553193472.0000
Epoch [250/10000], Loss: 39434149888.0000
Epoch [260/10000], Loss: 38440263680.0000
Epoch [270/10000], Loss: 37560139776.0000
Epoch [280/10000], Loss: 36782993408.0000
Epoch [290/10000], Loss: 36097740800.0000
Epoch [300/10000], Loss: 35494440960.0000
Epoch [310/10000], Loss: 34963111936.0000
Epoch [320/10000], Loss: 34494840832.0000
Epoch [330/10000], Loss: 34081173504.0000
Epoch [340/10000], Loss: 33714993152.0000
Epoch [350/10000], Loss: 33390108672.0000
Epoch [360/10000], Loss: 33100457984.0000
Epoch [370/10000], Loss: 32841717760.0000
Epoch [380/10000], Loss: 32609486848.0000
Epoch [390/10000], Loss: 32400347136.0000
Epoch [400/10000], Loss: 32210941952.0000
Epoch [410/10000], Loss: 32038375424.0000
Epoch [420/10000], Loss: 31880232960.0000
Epoch [430/10000], Loss: 31734411264.0000
Epoch [440/10000], Loss: 31598817280.0000
Epoch [450/10000], Loss: 31471691776.0000
Epoch [460/10000], Loss: 31352326144.0000
Epoch [470/10000], Loss: 31239507968.0000
Epoch [480/10000], Loss: 31132770304.0000
Epoch [490/10000], Loss: 31031689216.0000
Epoch [500/10000], Loss: 30935312384.0000
Epoch [510/10000], Loss: 30842927104.0000
Epoch [520/10000], Loss: 30754158592.0000
Epoch [530/10000], Loss: 30668654592.0000
Epoch [540/10000], Loss: 30586189824.0000
Epoch [550/10000], Loss: 30506569728.0000
Epoch [560/10000], Loss: 30429427712.0000
Epoch [570/10000], Loss: 30354374656.0000
Epoch [580/10000], Loss: 30281342976.0000
Epoch [590/10000], Loss: 30210076672.0000
Epoch [600/10000], Loss: 30140502016.0000
Epoch [610/10000], Loss: 30072526848.0000
Epoch [620/10000], Loss: 30005915648.0000
Epoch [630/10000], Loss: 29940422656.0000
Epoch [640/10000], Loss: 29875814400.0000
Epoch [650/10000], Loss: 29812146176.0000
Epoch [660/10000], Loss: 29749243904.0000
Epoch [670/10000], Loss: 29687371776.0000
Epoch [680/10000], Loss: 29626337280.0000
Epoch [690/10000], Loss: 29566353408.0000
Epoch [700/10000], Loss: 29507135488.0000
Epoch [710/10000], Loss: 29448697856.0000
Epoch [720/10000], Loss: 29391077376.0000
Epoch [730/10000], Loss: 29334106112.0000
Epoch [740/10000], Loss: 29277517824.0000
Epoch [750/10000], Loss: 29221152768.0000
Epoch [760/10000], Loss: 29165289472.0000
Epoch [770/10000], Loss: 29109514240.0000
Epoch [780/10000], Loss: 29053777920.0000
Epoch [790/10000], Loss: 28998371328.0000
Epoch [800/10000], Loss: 28942989312.0000
Epoch [810/10000], Loss: 28887760896.0000
Epoch [820/10000], Loss: 28832759808.0000
Epoch [830/10000], Loss: 28777783296.0000
Epoch [840/10000], Loss: 28723167232.0000
Epoch [850/10000], Loss: 28668645376.0000
Epoch [860/10000], Loss: 28613931008.0000
Epoch [870/10000], Loss: 28559278080.0000
Epoch [880/10000], Loss: 28505344000.0000
Epoch [890/10000], Loss: 28451870720.0000
Epoch [900/10000], Loss: 28398575616.0000
Epoch [910/10000], Loss: 28345452544.0000
Epoch [920/10000], Loss: 28292376576.0000
Epoch [930/10000], Loss: 28239382528.0000
Epoch [940/10000], Loss: 28186478592.0000
Epoch [950/10000], Loss: 28133419008.0000
Epoch [960/10000], Loss: 28080265216.0000
Epoch [970/10000], Loss: 28027324416.0000
Epoch [980/10000], Loss: 27974428672.0000
Epoch [990/10000], Loss: 27921641472.0000
Epoch [1000/10000], Loss: 27868985344.0000
Epoch [1010/10000], Loss: 27816202240.0000
Epoch [1020/10000], Loss: 27763116032.0000
Epoch [1030/10000], Loss: 27709845504.0000
Epoch [1040/10000], Loss: 27656546304.0000
Epoch [1050/10000], Loss: 27603286016.0000
Epoch [1060/10000], Loss: 27550144512.0000
Epoch [1070/10000], Loss: 27496970240.0000
Epoch [1080/10000], Loss: 27443752960.0000
Epoch [1090/10000], Loss: 27390631936.0000
Epoch [1100/10000], Loss: 27337426944.0000
Epoch [1110/10000], Loss: 27284236288.0000
Epoch [1120/10000], Loss: 27231059968.0000
Epoch [1130/10000], Loss: 27177678848.0000
Epoch [1140/10000], Loss: 27124379648.0000
Epoch [1150/10000], Loss: 27070978048.0000
Epoch [1160/10000], Loss: 27017254912.0000
Epoch [1170/10000], Loss: 26963578880.0000
Epoch [1180/10000], Loss: 26909933568.0000
Epoch [1190/10000], Loss: 26856103936.0000
Epoch [1200/10000], Loss: 26801776640.0000
Epoch [1210/10000], Loss: 26746687488.0000
Epoch [1220/10000], Loss: 26690914304.0000
Epoch [1230/10000], Loss: 26634422272.0000
Epoch [1240/10000], Loss: 26577299456.0000
Epoch [1250/10000], Loss: 26519674880.0000
Epoch [1260/10000], Loss: 26461413376.0000
Epoch [1270/10000], Loss: 26402387968.0000
Epoch [1280/10000], Loss: 26343202816.0000
Epoch [1290/10000], Loss: 26283976704.0000
Epoch [1300/10000], Loss: 26224678912.0000
Epoch [1310/10000], Loss: 26165026816.0000
Epoch [1320/10000], Loss: 26105061376.0000
Epoch [1330/10000], Loss: 26044751872.0000
Epoch [1340/10000], Loss: 25984067584.0000
Epoch [1350/10000], Loss: 25923155968.0000
Epoch [1360/10000], Loss: 25861971968.0000
Epoch [1370/10000], Loss: 25800706048.0000
Epoch [1380/10000], Loss: 25739409408.0000
Epoch [1390/10000], Loss: 25677963264.0000
Epoch [1400/10000], Loss: 25616107520.0000
Epoch [1410/10000], Loss: 25553678336.0000
Epoch [1420/10000], Loss: 25489989632.0000
Epoch [1430/10000], Loss: 25425082368.0000
Epoch [1440/10000], Loss: 25358571520.0000
Epoch [1450/10000], Loss: 25290956800.0000
Epoch [1460/10000], Loss: 25222920192.0000
Epoch [1470/10000], Loss: 25153998848.0000
Epoch [1480/10000], Loss: 25082609664.0000
Epoch [1490/10000], Loss: 25013000192.0000
Epoch [1500/10000], Loss: 24942618624.0000
Epoch [1510/10000], Loss: 24871286784.0000
Epoch [1520/10000], Loss: 24799436800.0000
Epoch [1530/10000], Loss: 24727476224.0000
Epoch [1540/10000], Loss: 24655945728.0000
Epoch [1550/10000], Loss: 24584853504.0000
Epoch [1560/10000], Loss: 24513675264.0000
Epoch [1570/10000], Loss: 24443078656.0000
Epoch [1580/10000], Loss: 24372166656.0000
Epoch [1590/10000], Loss: 24300632064.0000
Epoch [1600/10000], Loss: 24229189632.0000
Epoch [1610/10000], Loss: 24157954048.0000
Epoch [1620/10000], Loss: 24086927360.0000
Epoch [1630/10000], Loss: 24016021504.0000
Epoch [1640/10000], Loss: 23945369600.0000
Epoch [1650/10000], Loss: 23874996224.0000
Epoch [1660/10000], Loss: 23804323840.0000
Epoch [1670/10000], Loss: 23733319680.0000
Epoch [1680/10000], Loss: 23662077952.0000
Epoch [1690/10000], Loss: 23591053312.0000
Epoch [1700/10000], Loss: 23520391168.0000
Epoch [1710/10000], Loss: 23449720832.0000
Epoch [1720/10000], Loss: 23378688000.0000
Epoch [1730/10000], Loss: 23307786240.0000
Epoch [1740/10000], Loss: 23236585472.0000
Epoch [1750/10000], Loss: 23164921856.0000
Epoch [1760/10000], Loss: 23093024768.0000
Epoch [1770/10000], Loss: 23020957696.0000
Epoch [1780/10000], Loss: 22949011456.0000
Epoch [1790/10000], Loss: 22876897280.0000
Epoch [1800/10000], Loss: 22804723712.0000
Epoch [1810/10000], Loss: 22732040192.0000
Epoch [1820/10000], Loss: 22659487744.0000
Epoch [1830/10000], Loss: 22585982976.0000
Epoch [1840/10000], Loss: 22510757888.0000
Epoch [1850/10000], Loss: 22435039232.0000
Epoch [1860/10000], Loss: 22358675456.0000
Epoch [1870/10000], Loss: 22282219520.0000
Epoch [1880/10000], Loss: 22205505536.0000
Epoch [1890/10000], Loss: 22127888384.0000
Epoch [1900/10000], Loss: 22049689600.0000
Epoch [1910/10000], Loss: 21971152896.0000
Epoch [1920/10000], Loss: 21891864576.0000
Epoch [1930/10000], Loss: 21811134464.0000
Epoch [1940/10000], Loss: 21728698368.0000
Epoch [1950/10000], Loss: 21643464704.0000
Epoch [1960/10000], Loss: 21555875840.0000
Epoch [1970/10000], Loss: 21466712064.0000
Epoch [1980/10000], Loss: 21376112640.0000
Epoch [1990/10000], Loss: 21283313664.0000
Epoch [2000/10000], Loss: 21188759552.0000
Epoch [2010/10000], Loss: 21092435968.0000
Epoch [2020/10000], Loss: 20994107392.0000
Epoch [2030/10000], Loss: 20893739008.0000
Epoch [2040/10000], Loss: 20791101440.0000
Epoch [2050/10000], Loss: 20684789760.0000
Epoch [2060/10000], Loss: 20574599168.0000
Epoch [2070/10000], Loss: 20461643776.0000
Epoch [2080/10000], Loss: 20345655296.0000
Epoch [2090/10000], Loss: 20226410496.0000
Epoch [2100/10000], Loss: 20103139328.0000
Epoch [2110/10000], Loss: 19976273920.0000
Epoch [2120/10000], Loss: 19846488064.0000
Epoch [2130/10000], Loss: 19714979840.0000
Epoch [2140/10000], Loss: 19581093888.0000
Epoch [2150/10000], Loss: 19445839872.0000
Epoch [2160/10000], Loss: 19308279808.0000
Epoch [2170/10000], Loss: 19166873600.0000
Epoch [2180/10000], Loss: 19021690880.0000
Epoch [2190/10000], Loss: 18875041792.0000
Epoch [2200/10000], Loss: 18729256960.0000
Epoch [2210/10000], Loss: 18582267904.0000
Epoch [2220/10000], Loss: 18433560576.0000
Epoch [2230/10000], Loss: 18285217792.0000
Epoch [2240/10000], Loss: 18136616960.0000
Epoch [2250/10000], Loss: 17987756032.0000
Epoch [2260/10000], Loss: 17837690880.0000
Epoch [2270/10000], Loss: 17686052864.0000
Epoch [2280/10000], Loss: 17531879424.0000
Epoch [2290/10000], Loss: 17375600640.0000
Epoch [2300/10000], Loss: 17217865728.0000
Epoch [2310/10000], Loss: 17059779584.0000
Epoch [2320/10000], Loss: 16901090304.0000
Epoch [2330/10000], Loss: 16740779008.0000
Epoch [2340/10000], Loss: 16580083712.0000
Epoch [2350/10000], Loss: 16419234816.0000
Epoch [2360/10000], Loss: 16257790976.0000
Epoch [2370/10000], Loss: 16095084544.0000
Epoch [2380/10000], Loss: 15931526144.0000
Epoch [2390/10000], Loss: 15768222720.0000
Epoch [2400/10000], Loss: 15605518336.0000
Epoch [2410/10000], Loss: 15442877440.0000
Epoch [2420/10000], Loss: 15281520640.0000
Epoch [2430/10000], Loss: 15119843328.0000
Epoch [2440/10000], Loss: 14958709760.0000
Epoch [2450/10000], Loss: 14797286400.0000
Epoch [2460/10000], Loss: 14634669056.0000
Epoch [2470/10000], Loss: 14471868416.0000
Epoch [2480/10000], Loss: 14308187136.0000
Epoch [2490/10000], Loss: 14144666624.0000
Epoch [2500/10000], Loss: 13980708864.0000
Epoch [2510/10000], Loss: 13817159680.0000
Epoch [2520/10000], Loss: 13653061632.0000
Epoch [2530/10000], Loss: 13486576640.0000
Epoch [2540/10000], Loss: 13314198528.0000
Epoch [2550/10000], Loss: 13138299904.0000
Epoch [2560/10000], Loss: 12950436864.0000
Epoch [2570/10000], Loss: 12764236800.0000
Epoch [2580/10000], Loss: 12587486208.0000
Epoch [2590/10000], Loss: 12414268416.0000
Epoch [2600/10000], Loss: 12243549184.0000
Epoch [2610/10000], Loss: 12074473472.0000
Epoch [2620/10000], Loss: 11907896320.0000
Epoch [2630/10000], Loss: 11745350656.0000
Epoch [2640/10000], Loss: 11585308672.0000
Epoch [2650/10000], Loss: 11430145024.0000
Epoch [2660/10000], Loss: 11278936064.0000
Epoch [2670/10000], Loss: 11132106752.0000
Epoch [2680/10000], Loss: 10987561984.0000
Epoch [2690/10000], Loss: 10846710784.0000
Epoch [2700/10000], Loss: 10709570560.0000
Epoch [2710/10000], Loss: 10574758912.0000
Epoch [2720/10000], Loss: 10441657344.0000
Epoch [2730/10000], Loss: 10310834176.0000
Epoch [2740/10000], Loss: 10183040000.0000
Epoch [2750/10000], Loss: 10059055104.0000
Epoch [2760/10000], Loss: 9938721792.0000
Epoch [2770/10000], Loss: 9820856320.0000
Epoch [2780/10000], Loss: 9706504192.0000
Epoch [2790/10000], Loss: 9594488832.0000
Epoch [2800/10000], Loss: 9485031424.0000
Epoch [2810/10000], Loss: 9378639872.0000
Epoch [2820/10000], Loss: 9272140800.0000
Epoch [2830/10000], Loss: 9166880768.0000
Epoch [2840/10000], Loss: 9062151168.0000
Epoch [2850/10000], Loss: 8959307776.0000
Epoch [2860/10000], Loss: 8858350592.0000
Epoch [2870/10000], Loss: 8756534272.0000
Epoch [2880/10000], Loss: 8661329920.0000
Epoch [2890/10000], Loss: 8564367360.0000
Epoch [2900/10000], Loss: 8468385792.0000
Epoch [2910/10000], Loss: 8377244672.0000
Epoch [2920/10000], Loss: 8287601152.0000
Epoch [2930/10000], Loss: 8199349760.0000
Epoch [2940/10000], Loss: 8114779136.0000
Epoch [2950/10000], Loss: 8028698624.0000
Epoch [2960/10000], Loss: 7945261056.0000
Epoch [2970/10000], Loss: 7865004032.0000
Epoch [2980/10000], Loss: 7784518656.0000
Epoch [2990/10000], Loss: 7706725888.0000
Epoch [3000/10000], Loss: 7637889536.0000
Epoch [3010/10000], Loss: 7555919872.0000
Epoch [3020/10000], Loss: 7483069952.0000
Epoch [3030/10000], Loss: 7414327296.0000
Epoch [3040/10000], Loss: 7343202816.0000
Epoch [3050/10000], Loss: 7274842112.0000
Epoch [3060/10000], Loss: 7205709824.0000
Epoch [3070/10000], Loss: 7142199808.0000
Epoch [3080/10000], Loss: 7076720640.0000
Epoch [3090/10000], Loss: 7012910080.0000
Epoch [3100/10000], Loss: 6948173824.0000
Epoch [3110/10000], Loss: 6885165568.0000
Epoch [3120/10000], Loss: 6825755648.0000
Epoch [3130/10000], Loss: 6762736640.0000
Epoch [3140/10000], Loss: 6704155136.0000
Epoch [3150/10000], Loss: 6645750784.0000
Epoch [3160/10000], Loss: 6586749440.0000
Epoch [3170/10000], Loss: 6532270592.0000
Epoch [3180/10000], Loss: 6475646976.0000
Epoch [3190/10000], Loss: 6422570496.0000
Epoch [3200/10000], Loss: 6363180032.0000
Epoch [3210/10000], Loss: 6311463424.0000
Epoch [3220/10000], Loss: 6259980800.0000
Epoch [3230/10000], Loss: 6207683072.0000
Epoch [3240/10000], Loss: 6156358656.0000
Epoch [3250/10000], Loss: 6104972800.0000
Epoch [3260/10000], Loss: 6066157056.0000
Epoch [3270/10000], Loss: 6009049088.0000
Epoch [3280/10000], Loss: 5961495552.0000
Epoch [3290/10000], Loss: 5915039232.0000
Epoch [3300/10000], Loss: 5866169856.0000
Epoch [3310/10000], Loss: 5824683008.0000
Epoch [3320/10000], Loss: 5817724928.0000
Epoch [3330/10000], Loss: 5741366784.0000
Epoch [3340/10000], Loss: 5691905024.0000
Epoch [3350/10000], Loss: 5646948352.0000
Epoch [3360/10000], Loss: 5613484032.0000
Epoch [3370/10000], Loss: 5572694016.0000
Epoch [3380/10000], Loss: 5531217408.0000
Epoch [3390/10000], Loss: 5489067520.0000
Epoch [3400/10000], Loss: 5450274816.0000
Epoch [3410/10000], Loss: 5413888000.0000
Epoch [3420/10000], Loss: 5378019328.0000
Epoch [3430/10000], Loss: 5348468224.0000
Epoch [3440/10000], Loss: 5337790976.0000
Epoch [3450/10000], Loss: 5279621632.0000
Epoch [3460/10000], Loss: 5238903808.0000
Epoch [3470/10000], Loss: 5206509568.0000
Epoch [3480/10000], Loss: 5177607680.0000
Epoch [3490/10000], Loss: 5147188224.0000
Epoch [3500/10000], Loss: 5117051392.0000
Epoch [3510/10000], Loss: 5095810048.0000
Epoch [3520/10000], Loss: 5059245056.0000
Epoch [3530/10000], Loss: 5024903680.0000
Epoch [3540/10000], Loss: 4995672576.0000
Epoch [3550/10000], Loss: 4965655040.0000
Epoch [3560/10000], Loss: 4932336128.0000
Epoch [3570/10000], Loss: 4910925312.0000
Epoch [3580/10000], Loss: 4873300480.0000
Epoch [3590/10000], Loss: 4845272064.0000
Epoch [3600/10000], Loss: 4815678464.0000
Epoch [3610/10000], Loss: 4785000448.0000
Epoch [3620/10000], Loss: 4762200064.0000
Epoch [3630/10000], Loss: 4739947008.0000
Epoch [3640/10000], Loss: 4725755392.0000
Epoch [3650/10000], Loss: 4678748160.0000
Epoch [3660/10000], Loss: 4651635712.0000
Epoch [3670/10000], Loss: 4631448064.0000
Epoch [3680/10000], Loss: 4606099456.0000
Epoch [3690/10000], Loss: 4586647040.0000
Epoch [3700/10000], Loss: 4559008256.0000
Epoch [3710/10000], Loss: 4532440064.0000
Epoch [3720/10000], Loss: 4504173056.0000
Epoch [3730/10000], Loss: 4478772224.0000
Epoch [3740/10000], Loss: 4490721792.0000
Epoch [3750/10000], Loss: 4441214464.0000
Epoch [3760/10000], Loss: 4415414272.0000
Epoch [3770/10000], Loss: 4388819456.0000
Epoch [3780/10000], Loss: 4371911168.0000
Epoch [3790/10000], Loss: 4343999488.0000
Epoch [3800/10000], Loss: 4341228544.0000
Epoch [3810/10000], Loss: 4310995968.0000
Epoch [3820/10000], Loss: 4284592896.0000
Epoch [3830/10000], Loss: 4260595712.0000
Epoch [3840/10000], Loss: 4253179136.0000
Epoch [3850/10000], Loss: 4264627456.0000
Epoch [3860/10000], Loss: 4204399360.0000
Epoch [3870/10000], Loss: 4187966720.0000
Epoch [3880/10000], Loss: 4167394560.0000
Epoch [3890/10000], Loss: 4144654848.0000
Epoch [3900/10000], Loss: 4139485952.0000
Epoch [3910/10000], Loss: 4110624512.0000
Epoch [3920/10000], Loss: 4090775040.0000
Epoch [3930/10000], Loss: 4073754112.0000
Epoch [3940/10000], Loss: 4064495872.0000
Epoch [3950/10000], Loss: 4046294272.0000
Epoch [3960/10000], Loss: 4024271616.0000
Epoch [3970/10000], Loss: 4007167744.0000
Epoch [3980/10000], Loss: 3992166144.0000
Epoch [3990/10000], Loss: 3982580224.0000
Epoch [4000/10000], Loss: 3960576768.0000
Epoch [4010/10000], Loss: 3951496704.0000
Epoch [4020/10000], Loss: 3928729600.0000
Epoch [4030/10000], Loss: 3911944448.0000
Epoch [4040/10000], Loss: 3898537984.0000
Epoch [4050/10000], Loss: 3877900288.0000
Epoch [4060/10000], Loss: 3867506688.0000
Epoch [4070/10000], Loss: 3854564352.0000
Epoch [4080/10000], Loss: 3833911808.0000
Epoch [4090/10000], Loss: 3827783424.0000
Epoch [4100/10000], Loss: 3806002944.0000
Epoch [4110/10000], Loss: 3815466752.0000
Epoch [4120/10000], Loss: 3791600640.0000
Epoch [4130/10000], Loss: 3769274368.0000
Epoch [4140/10000], Loss: 3754488320.0000
Epoch [4150/10000], Loss: 3745528064.0000
Epoch [4160/10000], Loss: 3730535680.0000
Epoch [4170/10000], Loss: 3724446208.0000
Epoch [4180/10000], Loss: 3711760640.0000
Epoch [4190/10000], Loss: 3693253120.0000
Epoch [4200/10000], Loss: 3677035264.0000
Epoch [4210/10000], Loss: 3682590976.0000
Epoch [4220/10000], Loss: 3652941824.0000
Epoch [4230/10000], Loss: 3643554816.0000
Epoch [4240/10000], Loss: 3627898112.0000
Epoch [4250/10000], Loss: 3633274112.0000
Epoch [4260/10000], Loss: 3608836864.0000
Epoch [4270/10000], Loss: 3591535104.0000
Epoch [4280/10000], Loss: 3583310080.0000
Epoch [4290/10000], Loss: 3578410240.0000
Epoch [4300/10000], Loss: 3558219776.0000
Epoch [4310/10000], Loss: 3562994432.0000
Epoch [4320/10000], Loss: 3536808960.0000
Epoch [4330/10000], Loss: 3526701312.0000
Epoch [4340/10000], Loss: 3521273856.0000
Epoch [4350/10000], Loss: 3505239808.0000
Epoch [4360/10000], Loss: 3503477760.0000
Epoch [4370/10000], Loss: 3485618432.0000
Epoch [4380/10000], Loss: 3470586880.0000
Epoch [4390/10000], Loss: 3465895168.0000
Epoch [4400/10000], Loss: 3461206528.0000
Epoch [4410/10000], Loss: 3447182336.0000
Epoch [4420/10000], Loss: 3433201664.0000
Epoch [4430/10000], Loss: 3429462272.0000
Epoch [4440/10000], Loss: 3413472256.0000
Epoch [4450/10000], Loss: 3400540160.0000
Epoch [4460/10000], Loss: 3393595392.0000
Epoch [4470/10000], Loss: 3382243584.0000
Epoch [4480/10000], Loss: 3395930112.0000
Epoch [4490/10000], Loss: 3368309248.0000
Epoch [4500/10000], Loss: 3352822784.0000
Epoch [4510/10000], Loss: 3347123712.0000
Epoch [4520/10000], Loss: 3330389504.0000
Epoch [4530/10000], Loss: 3332773376.0000
Epoch [4540/10000], Loss: 3321235456.0000
Epoch [4550/10000], Loss: 3303904768.0000
Epoch [4560/10000], Loss: 3306193152.0000
Epoch [4570/10000], Loss: 3290996992.0000
Epoch [4580/10000], Loss: 3280463104.0000
Epoch [4590/10000], Loss: 3271142400.0000
Epoch [4600/10000], Loss: 3265650944.0000
Epoch [4610/10000], Loss: 3248799232.0000
Epoch [4620/10000], Loss: 3279291136.0000
Epoch [4630/10000], Loss: 3248885760.0000
Epoch [4640/10000], Loss: 3234096384.0000
Epoch [4650/10000], Loss: 3219590400.0000
Epoch [4660/10000], Loss: 3209147904.0000
Epoch [4670/10000], Loss: 3210303232.0000
Epoch [4680/10000], Loss: 3196377600.0000
Epoch [4690/10000], Loss: 3187228672.0000
Epoch [4700/10000], Loss: 3177142528.0000
Epoch [4710/10000], Loss: 3169658368.0000
Epoch [4720/10000], Loss: 3170015744.0000
Epoch [4730/10000], Loss: 3152867072.0000
Epoch [4740/10000], Loss: 3152425216.0000
Epoch [4750/10000], Loss: 3145518848.0000
Epoch [4760/10000], Loss: 3138225152.0000
Epoch [4770/10000], Loss: 3125236480.0000
Epoch [4780/10000], Loss: 3131133440.0000
Epoch [4790/10000], Loss: 3133558016.0000
Epoch [4800/10000], Loss: 3114557696.0000
Epoch [4810/10000], Loss: 3098081280.0000
Epoch [4820/10000], Loss: 3095632128.0000
Epoch [4830/10000], Loss: 3086028800.0000
Epoch [4840/10000], Loss: 3077510144.0000
Epoch [4850/10000], Loss: 3073416192.0000
Epoch [4860/10000], Loss: 3069588736.0000
Epoch [4870/10000], Loss: 3061447424.0000
Epoch [4880/10000], Loss: 3055464704.0000
Epoch [4890/10000], Loss: 3051583232.0000
Epoch [4900/10000], Loss: 3042648320.0000
Epoch [4910/10000], Loss: 3030856960.0000
Epoch [4920/10000], Loss: 3037825792.0000
Epoch [4930/10000], Loss: 3029419008.0000
Epoch [4940/10000], Loss: 3008811776.0000
Epoch [4950/10000], Loss: 3006854400.0000
Epoch [4960/10000], Loss: 3003416832.0000
Epoch [4970/10000], Loss: 2992418816.0000
Epoch [4980/10000], Loss: 2995773696.0000
Epoch [4990/10000], Loss: 2991357440.0000
Epoch [5000/10000], Loss: 2969836288.0000
Epoch [5010/10000], Loss: 2980129280.0000
Epoch [5020/10000], Loss: 2960833024.0000
Epoch [5030/10000], Loss: 2952809728.0000
Epoch [5040/10000], Loss: 2957916672.0000
Epoch [5050/10000], Loss: 2940487936.0000
Epoch [5060/10000], Loss: 2940962304.0000
Epoch [5070/10000], Loss: 2934764800.0000
Epoch [5080/10000], Loss: 2928993536.0000
Epoch [5090/10000], Loss: 2931434240.0000
Epoch [5100/10000], Loss: 2919537408.0000
Epoch [5110/10000], Loss: 2906075648.0000
Epoch [5120/10000], Loss: 2927224064.0000
Epoch [5130/10000], Loss: 2902827520.0000
Epoch [5140/10000], Loss: 2896520704.0000
Epoch [5150/10000], Loss: 2883586816.0000
Epoch [5160/10000], Loss: 2886972160.0000
Epoch [5170/10000], Loss: 2867039232.0000
Epoch [5180/10000], Loss: 2873439488.0000
Epoch [5190/10000], Loss: 2861988864.0000
Epoch [5200/10000], Loss: 2852918528.0000
Epoch [5210/10000], Loss: 2860087552.0000
Epoch [5220/10000], Loss: 2839692800.0000
Epoch [5230/10000], Loss: 2847145984.0000
Epoch [5240/10000], Loss: 2838629632.0000
Epoch [5250/10000], Loss: 2828067072.0000
Epoch [5260/10000], Loss: 2832134400.0000
Epoch [5270/10000], Loss: 2820568832.0000
Epoch [5280/10000], Loss: 2813483264.0000
Epoch [5290/10000], Loss: 2801124352.0000
Epoch [5300/10000], Loss: 2809410560.0000
Epoch [5310/10000], Loss: 2799358208.0000
Epoch [5320/10000], Loss: 2783502336.0000
Epoch [5330/10000], Loss: 2789405952.0000
Epoch [5340/10000], Loss: 2781915136.0000
Epoch [5350/10000], Loss: 2788878848.0000
Epoch [5360/10000], Loss: 2764881664.0000
Epoch [5370/10000], Loss: 2756023296.0000
Epoch [5380/10000], Loss: 2753573376.0000
Epoch [5390/10000], Loss: 2752476160.0000
Epoch [5400/10000], Loss: 2740486656.0000
Epoch [5410/10000], Loss: 2750493952.0000
Epoch [5420/10000], Loss: 2733755392.0000
Epoch [5430/10000], Loss: 2737707776.0000
Epoch [5440/10000], Loss: 2747668992.0000
Epoch [5450/10000], Loss: 2731519232.0000
Epoch [5460/10000], Loss: 2711805952.0000
Epoch [5470/10000], Loss: 2714754048.0000
Epoch [5480/10000], Loss: 2704708352.0000
Epoch [5490/10000], Loss: 2696717056.0000
Epoch [5500/10000], Loss: 2712837632.0000
Epoch [5510/10000], Loss: 2688124416.0000
Epoch [5520/10000], Loss: 2685782272.0000
Epoch [5530/10000], Loss: 2687791360.0000
Epoch [5540/10000], Loss: 2673448704.0000
Epoch [5550/10000], Loss: 2681891072.0000
Epoch [5560/10000], Loss: 2665351424.0000
Epoch [5570/10000], Loss: 2670198528.0000
Epoch [5580/10000], Loss: 2658390528.0000
Epoch [5590/10000], Loss: 2659404032.0000
Epoch [5600/10000], Loss: 2657780480.0000
Epoch [5610/10000], Loss: 2646512896.0000
Epoch [5620/10000], Loss: 2659298816.0000
Epoch [5630/10000], Loss: 2638614784.0000
Epoch [5640/10000], Loss: 2633453056.0000
Epoch [5650/10000], Loss: 2630362368.0000
Epoch [5660/10000], Loss: 2630851840.0000
Epoch [5670/10000], Loss: 2618382080.0000
Epoch [5680/10000], Loss: 2624424192.0000
Epoch [5690/10000], Loss: 2606528512.0000
Epoch [5700/10000], Loss: 2617516288.0000
Epoch [5710/10000], Loss: 2610330368.0000
Epoch [5720/10000], Loss: 2595715584.0000
Epoch [5730/10000], Loss: 2593130240.0000
Epoch [5740/10000], Loss: 2596262400.0000
Epoch [5750/10000], Loss: 2583693056.0000
Epoch [5760/10000], Loss: 2584419072.0000
Epoch [5770/10000], Loss: 2575296256.0000
Epoch [5780/10000], Loss: 2578001408.0000
Epoch [5790/10000], Loss: 2568302848.0000
Epoch [5800/10000], Loss: 2565744896.0000
Epoch [5810/10000], Loss: 2559791104.0000
Epoch [5820/10000], Loss: 2560289024.0000
Epoch [5830/10000], Loss: 2546059008.0000
Epoch [5840/10000], Loss: 2557901824.0000
Epoch [5850/10000], Loss: 2541013248.0000
Epoch [5860/10000], Loss: 2544367872.0000
Epoch [5870/10000], Loss: 2552157184.0000
Epoch [5880/10000], Loss: 2541111552.0000
Epoch [5890/10000], Loss: 2525993984.0000
Epoch [5900/10000], Loss: 2526525952.0000
Epoch [5910/10000], Loss: 2514047488.0000
Epoch [5920/10000], Loss: 2531786752.0000
Epoch [5930/10000], Loss: 2507421440.0000
Epoch [5940/10000], Loss: 2506919936.0000
Epoch [5950/10000], Loss: 2505056000.0000
Epoch [5960/10000], Loss: 2496488448.0000
Epoch [5970/10000], Loss: 2500248576.0000
Epoch [5980/10000], Loss: 2528514048.0000
Epoch [5990/10000], Loss: 2496539904.0000
Epoch [6000/10000], Loss: 2490183680.0000
Epoch [6010/10000], Loss: 2480079616.0000
Epoch [6020/10000], Loss: 2475218944.0000
Epoch [6030/10000], Loss: 2477799424.0000
Epoch [6040/10000], Loss: 2471250432.0000
Epoch [6050/10000], Loss: 2462772736.0000
Epoch [6060/10000], Loss: 2462651648.0000
Epoch [6070/10000], Loss: 2462771712.0000
Epoch [6080/10000], Loss: 2461954560.0000
Epoch [6090/10000], Loss: 2448605440.0000
Epoch [6100/10000], Loss: 2453399552.0000
Epoch [6110/10000], Loss: 2445027328.0000
Epoch [6120/10000], Loss: 2474389504.0000
Epoch [6130/10000], Loss: 2447848448.0000
Epoch [6140/10000], Loss: 2435211008.0000
Epoch [6150/10000], Loss: 2435099136.0000
Epoch [6160/10000], Loss: 2421842176.0000
Epoch [6170/10000], Loss: 2424975104.0000
Epoch [6180/10000], Loss: 2419620608.0000
Epoch [6190/10000], Loss: 2418585856.0000
Epoch [6200/10000], Loss: 2413898752.0000
Epoch [6210/10000], Loss: 2411735040.0000
Epoch [6220/10000], Loss: 2413238784.0000
Epoch [6230/10000], Loss: 2400744960.0000
Epoch [6240/10000], Loss: 2411193856.0000
Epoch [6250/10000], Loss: 2388815104.0000
Epoch [6260/10000], Loss: 2398650880.0000
Epoch [6270/10000], Loss: 2387508224.0000
Epoch [6280/10000], Loss: 2393847296.0000
Epoch [6290/10000], Loss: 2381820672.0000
Epoch [6300/10000], Loss: 2387097856.0000
Epoch [6310/10000], Loss: 2378995968.0000
Epoch [6320/10000], Loss: 2374241024.0000
Epoch [6330/10000], Loss: 2363242496.0000
Epoch [6340/10000], Loss: 2386127360.0000
Epoch [6350/10000], Loss: 2365661696.0000
Epoch [6360/10000], Loss: 2353520640.0000
Epoch [6370/10000], Loss: 2358082048.0000
Epoch [6380/10000], Loss: 2346872576.0000
Epoch [6390/10000], Loss: 2360679168.0000
Epoch [6400/10000], Loss: 2341180160.0000
Epoch [6410/10000], Loss: 2388291072.0000
Epoch [6420/10000], Loss: 2346024704.0000
Epoch [6430/10000], Loss: 2347440384.0000
Epoch [6440/10000], Loss: 2327400960.0000
Epoch [6450/10000], Loss: 2329484032.0000
Epoch [6460/10000], Loss: 2323392768.0000
Epoch [6470/10000], Loss: 2322240000.0000
Epoch [6480/10000], Loss: 2323016960.0000
Epoch [6490/10000], Loss: 2321670400.0000
Epoch [6500/10000], Loss: 2310045952.0000
Epoch [6510/10000], Loss: 2316552960.0000
Epoch [6520/10000], Loss: 2304182784.0000
Epoch [6530/10000], Loss: 2310857472.0000
Epoch [6540/10000], Loss: 2301212672.0000
Epoch [6550/10000], Loss: 2308807424.0000
Epoch [6560/10000], Loss: 2292982528.0000
Epoch [6570/10000], Loss: 2311119360.0000
Epoch [6580/10000], Loss: 2294327296.0000
Epoch [6590/10000], Loss: 2294208256.0000
Epoch [6600/10000], Loss: 2282809344.0000
Epoch [6610/10000], Loss: 2282838528.0000
Epoch [6620/10000], Loss: 2277477888.0000
Epoch [6630/10000], Loss: 2299635200.0000
Epoch [6640/10000], Loss: 2283662080.0000
Epoch [6650/10000], Loss: 2271457792.0000
Epoch [6660/10000], Loss: 2262554368.0000
Epoch [6670/10000], Loss: 2281238528.0000
Epoch [6680/10000], Loss: 2260258560.0000
Epoch [6690/10000], Loss: 2261393920.0000
Epoch [6700/10000], Loss: 2260733696.0000
Epoch [6710/10000], Loss: 2249516800.0000
Epoch [6720/10000], Loss: 2272617216.0000
Epoch [6730/10000], Loss: 2250735360.0000
Epoch [6740/10000], Loss: 2240633344.0000
Epoch [6750/10000], Loss: 2248001024.0000
Epoch [6760/10000], Loss: 2246560000.0000
Epoch [6770/10000], Loss: 2253022976.0000
Epoch [6780/10000], Loss: 2236257280.0000
Epoch [6790/10000], Loss: 2229622528.0000
Epoch [6800/10000], Loss: 2228794112.0000
Epoch [6810/10000], Loss: 2225636352.0000
Epoch [6820/10000], Loss: 2238477568.0000
Epoch [6830/10000], Loss: 2215875072.0000
Epoch [6840/10000], Loss: 2230727936.0000
Epoch [6850/10000], Loss: 2213054720.0000
Epoch [6860/10000], Loss: 2221588992.0000
Epoch [6870/10000], Loss: 2205377024.0000
Epoch [6880/10000], Loss: 2213481728.0000
Epoch [6890/10000], Loss: 2215842816.0000
Epoch [6900/10000], Loss: 2204030208.0000
Epoch [6910/10000], Loss: 2242064384.0000
Epoch [6920/10000], Loss: 2202931456.0000
Epoch [6930/10000], Loss: 2191331328.0000
Epoch [6940/10000], Loss: 2199475968.0000
Epoch [6950/10000], Loss: 2185612288.0000
Epoch [6960/10000], Loss: 2193490176.0000
Epoch [6970/10000], Loss: 2188136448.0000
Epoch [6980/10000], Loss: 2180539392.0000
Epoch [6990/10000], Loss: 2194154752.0000
Epoch [7000/10000], Loss: 2181321984.0000
Epoch [7010/10000], Loss: 2174782464.0000
Epoch [7020/10000], Loss: 2181085184.0000
Epoch [7030/10000], Loss: 2168049152.0000
Epoch [7040/10000], Loss: 2187429632.0000
Epoch [7050/10000], Loss: 2182571264.0000
Epoch [7060/10000], Loss: 2163437568.0000
Epoch [7070/10000], Loss: 2169222912.0000
Epoch [7080/10000], Loss: 2155181824.0000
Epoch [7090/10000], Loss: 2166765056.0000
Epoch [7100/10000], Loss: 2149235456.0000
Epoch [7110/10000], Loss: 2160807424.0000
Epoch [7120/10000], Loss: 2148117248.0000
Epoch [7130/10000], Loss: 2166810368.0000
Epoch [7140/10000], Loss: 2161579776.0000
Epoch [7150/10000], Loss: 2143908352.0000
Epoch [7160/10000], Loss: 2137140864.0000
Epoch [7170/10000], Loss: 2169488640.0000
Epoch [7180/10000], Loss: 2134905344.0000
Epoch [7190/10000], Loss: 2129259520.0000
Epoch [7200/10000], Loss: 2132582784.0000
Epoch [7210/10000], Loss: 2130287360.0000
Epoch [7220/10000], Loss: 2133077504.0000
Epoch [7230/10000], Loss: 2128887680.0000
Epoch [7240/10000], Loss: 2118246784.0000
Epoch [7250/10000], Loss: 2124708096.0000
Epoch [7260/10000], Loss: 2116986624.0000
Epoch [7270/10000], Loss: 2142621824.0000
Epoch [7280/10000], Loss: 2112934144.0000
Epoch [7290/10000], Loss: 2108426496.0000
Epoch [7300/10000], Loss: 2141994112.0000
Epoch [7310/10000], Loss: 2111977088.0000
Epoch [7320/10000], Loss: 2099641216.0000
Epoch [7330/10000], Loss: 2101525504.0000
Epoch [7340/10000], Loss: 2101151360.0000
Epoch [7350/10000], Loss: 2094671872.0000
Epoch [7360/10000], Loss: 2105047296.0000
Epoch [7370/10000], Loss: 2089033472.0000
Epoch [7380/10000], Loss: 2097452800.0000
Epoch [7390/10000], Loss: 2092215424.0000
Epoch [7400/10000], Loss: 2124631168.0000
Epoch [7410/10000], Loss: 2095457152.0000
Epoch [7420/10000], Loss: 2086641664.0000
Epoch [7430/10000], Loss: 2077890688.0000
Epoch [7440/10000], Loss: 2073734272.0000
Epoch [7450/10000], Loss: 2092171520.0000
Epoch [7460/10000], Loss: 2074749952.0000
Epoch [7470/10000], Loss: 2066467968.0000
Epoch [7480/10000], Loss: 2072594816.0000
Epoch [7490/10000], Loss: 2066668032.0000
Epoch [7500/10000], Loss: 2093650432.0000
Epoch [7510/10000], Loss: 2070081280.0000
Epoch [7520/10000], Loss: 2060846080.0000
Epoch [7530/10000], Loss: 2055336064.0000
Epoch [7540/10000], Loss: 2057551872.0000
Epoch [7550/10000], Loss: 2063753472.0000
Epoch [7560/10000], Loss: 2054878464.0000
Epoch [7570/10000], Loss: 2057580416.0000
Epoch [7580/10000], Loss: 2050848128.0000
Epoch [7590/10000], Loss: 2054713344.0000
Epoch [7600/10000], Loss: 2038613248.0000
Epoch [7610/10000], Loss: 2050299392.0000
Epoch [7620/10000], Loss: 2037352576.0000
Epoch [7630/10000], Loss: 2076525824.0000
Epoch [7640/10000], Loss: 2048320256.0000
Epoch [7650/10000], Loss: 2033467776.0000
Epoch [7660/10000], Loss: 2029393152.0000
Epoch [7670/10000], Loss: 2056397696.0000
Epoch [7680/10000], Loss: 2022746368.0000
Epoch [7690/10000], Loss: 2027116160.0000
Epoch [7700/10000], Loss: 2025189888.0000
Epoch [7710/10000], Loss: 2019562880.0000
Epoch [7720/10000], Loss: 2020281984.0000
Epoch [7730/10000], Loss: 2038843520.0000
Epoch [7740/10000], Loss: 2033569280.0000
Epoch [7750/10000], Loss: 2012242176.0000
Epoch [7760/10000], Loss: 2012098560.0000
Epoch [7770/10000], Loss: 2009663872.0000
Epoch [7780/10000], Loss: 2014582016.0000
Epoch [7790/10000], Loss: 2014556160.0000
Epoch [7800/10000], Loss: 2032128640.0000
Epoch [7810/10000], Loss: 2011707776.0000
Epoch [7820/10000], Loss: 2002769408.0000
Epoch [7830/10000], Loss: 1997468288.0000
Epoch [7840/10000], Loss: 2001800064.0000
Epoch [7850/10000], Loss: 1992898048.0000
Epoch [7860/10000], Loss: 2003023744.0000
Epoch [7870/10000], Loss: 1991664384.0000
Epoch [7880/10000], Loss: 1995576960.0000
Epoch [7890/10000], Loss: 1989745920.0000
Epoch [7900/10000], Loss: 1990721792.0000
Epoch [7910/10000], Loss: 1992482176.0000
Epoch [7920/10000], Loss: 1988452224.0000
Epoch [7930/10000], Loss: 1990443136.0000
Epoch [7940/10000], Loss: 1972082560.0000
Epoch [7950/10000], Loss: 1981559552.0000
Epoch [7960/10000], Loss: 1973150464.0000
Epoch [7970/10000], Loss: 1999383296.0000
Epoch [7980/10000], Loss: 1967027200.0000
Epoch [7990/10000], Loss: 1981695488.0000
Epoch [8000/10000], Loss: 1965170688.0000
Epoch [8010/10000], Loss: 1974881536.0000
Epoch [8020/10000], Loss: 1961249408.0000
Epoch [8030/10000], Loss: 1965836032.0000
Epoch [8040/10000], Loss: 1961008000.0000
Epoch [8050/10000], Loss: 1962919680.0000
Epoch [8060/10000], Loss: 1956431616.0000
Epoch [8070/10000], Loss: 1959559168.0000
Epoch [8080/10000], Loss: 1965271808.0000
Epoch [8090/10000], Loss: 1966480384.0000
Epoch [8100/10000], Loss: 1952707200.0000
Epoch [8110/10000], Loss: 1942926208.0000
Epoch [8120/10000], Loss: 1958801024.0000
Epoch [8130/10000], Loss: 1947213184.0000
Epoch [8140/10000], Loss: 1946152064.0000
Epoch [8150/10000], Loss: 1947266816.0000
Epoch [8160/10000], Loss: 1932903552.0000
Epoch [8170/10000], Loss: 1947252352.0000
Epoch [8180/10000], Loss: 1948906496.0000
Epoch [8190/10000], Loss: 1935009408.0000
Epoch [8200/10000], Loss: 1928837888.0000
Epoch [8210/10000], Loss: 1926797824.0000
Epoch [8220/10000], Loss: 1933050880.0000
Epoch [8230/10000], Loss: 1941895680.0000
Epoch [8240/10000], Loss: 1933791616.0000
Epoch [8250/10000], Loss: 1924434304.0000
Epoch [8260/10000], Loss: 1918769664.0000
Epoch [8270/10000], Loss: 1921701632.0000
Epoch [8280/10000], Loss: 1919624320.0000
Epoch [8290/10000], Loss: 1936179712.0000
Epoch [8300/10000], Loss: 1915673728.0000
Epoch [8310/10000], Loss: 1911277824.0000
Epoch [8320/10000], Loss: 1925158912.0000
Epoch [8330/10000], Loss: 1907324032.0000
Epoch [8340/10000], Loss: 1911923328.0000
Epoch [8350/10000], Loss: 1910498048.0000
Epoch [8360/10000], Loss: 1926257408.0000
Epoch [8370/10000], Loss: 1899247744.0000
Epoch [8380/10000], Loss: 1907998720.0000
Epoch [8390/10000], Loss: 1896338432.0000
Epoch [8400/10000], Loss: 1906887424.0000
Epoch [8410/10000], Loss: 1891608192.0000
Epoch [8420/10000], Loss: 1920566400.0000
Epoch [8430/10000], Loss: 1911195904.0000
Epoch [8440/10000], Loss: 1886211712.0000
Epoch [8450/10000], Loss: 1890839296.0000
Epoch [8460/10000], Loss: 1888867072.0000
Epoch [8470/10000], Loss: 1898294400.0000
Epoch [8480/10000], Loss: 1902750848.0000
Epoch [8490/10000], Loss: 1881236096.0000
Epoch [8500/10000], Loss: 1879642880.0000
Epoch [8510/10000], Loss: 1878988032.0000
Epoch [8520/10000], Loss: 1884136192.0000
Epoch [8530/10000], Loss: 1920661888.0000
Epoch [8540/10000], Loss: 1879233408.0000
Epoch [8550/10000], Loss: 1873678592.0000
Epoch [8560/10000], Loss: 1867909376.0000
Epoch [8570/10000], Loss: 1869823232.0000
Epoch [8580/10000], Loss: 1864174080.0000
Epoch [8590/10000], Loss: 1873981568.0000
Epoch [8600/10000], Loss: 1864399104.0000
Epoch [8610/10000], Loss: 1888421888.0000
Epoch [8620/10000], Loss: 1868284416.0000
Epoch [8630/10000], Loss: 1856559232.0000
Epoch [8640/10000], Loss: 1865983360.0000
Epoch [8650/10000], Loss: 1857273856.0000
Epoch [8660/10000], Loss: 1870807168.0000
Epoch [8670/10000], Loss: 1851502592.0000
Epoch [8680/10000], Loss: 1864405760.0000
Epoch [8690/10000], Loss: 1861639296.0000
Epoch [8700/10000], Loss: 1855030144.0000
Epoch [8710/10000], Loss: 1855192832.0000
Epoch [8720/10000], Loss: 1844568320.0000
Epoch [8730/10000], Loss: 1847132160.0000
Epoch [8740/10000], Loss: 1851677568.0000
Epoch [8750/10000], Loss: 1853071616.0000
Epoch [8760/10000], Loss: 1847882624.0000
Epoch [8770/10000], Loss: 1848881152.0000
Epoch [8780/10000], Loss: 1834388480.0000
Epoch [8790/10000], Loss: 1850166400.0000
Epoch [8800/10000], Loss: 1837379456.0000
Epoch [8810/10000], Loss: 1833959040.0000
Epoch [8820/10000], Loss: 1861201792.0000
Epoch [8830/10000], Loss: 1825754240.0000
Epoch [8840/10000], Loss: 1834425856.0000
Epoch [8850/10000], Loss: 1828756096.0000
Epoch [8860/10000], Loss: 1827217920.0000
Epoch [8870/10000], Loss: 1826100224.0000
Epoch [8880/10000], Loss: 1837380352.0000
Epoch [8890/10000], Loss: 1833058560.0000
Epoch [8900/10000], Loss: 1817696512.0000
Epoch [8910/10000], Loss: 1820902016.0000
Epoch [8920/10000], Loss: 1827321088.0000
Epoch [8930/10000], Loss: 1839978880.0000
Epoch [8940/10000], Loss: 1816458496.0000
Epoch [8950/10000], Loss: 1813110144.0000
Epoch [8960/10000], Loss: 1810810368.0000
Epoch [8970/10000], Loss: 1810064896.0000
Epoch [8980/10000], Loss: 1818519040.0000
Epoch [8990/10000], Loss: 1811172736.0000
Epoch [9000/10000], Loss: 1811548160.0000
Epoch [9010/10000], Loss: 1809094400.0000
Epoch [9020/10000], Loss: 1806740480.0000
Epoch [9030/10000], Loss: 1810307200.0000
Epoch [9040/10000], Loss: 1801981184.0000
Epoch [9050/10000], Loss: 1806774400.0000
Epoch [9060/10000], Loss: 1801376768.0000
Epoch [9070/10000], Loss: 1797924864.0000
Epoch [9080/10000], Loss: 1810909056.0000
Epoch [9090/10000], Loss: 1789628544.0000
Epoch [9100/10000], Loss: 1808957568.0000
Epoch [9110/10000], Loss: 1784388608.0000
Epoch [9120/10000], Loss: 1795287040.0000
Epoch [9130/10000], Loss: 1786632064.0000
Epoch [9140/10000], Loss: 1821871360.0000
Epoch [9150/10000], Loss: 1791533952.0000
Epoch [9160/10000], Loss: 1783282176.0000
Epoch [9170/10000], Loss: 1783583744.0000
Epoch [9180/10000], Loss: 1782998528.0000
Epoch [9190/10000], Loss: 1776673408.0000
Epoch [9200/10000], Loss: 1796944512.0000
Epoch [9210/10000], Loss: 1778765184.0000
Epoch [9220/10000], Loss: 1781335808.0000
Epoch [9230/10000], Loss: 1770808576.0000
Epoch [9240/10000], Loss: 1779689856.0000
Epoch [9250/10000], Loss: 1769881856.0000
Epoch [9260/10000], Loss: 1781874688.0000
Epoch [9270/10000], Loss: 1762339840.0000
Epoch [9280/10000], Loss: 1798985984.0000
Epoch [9290/10000], Loss: 1760763776.0000
Epoch [9300/10000], Loss: 1758655104.0000
Epoch [9310/10000], Loss: 1759983488.0000
Epoch [9320/10000], Loss: 1761568768.0000
Epoch [9330/10000], Loss: 1764603264.0000
Epoch [9340/10000], Loss: 1764026112.0000
Epoch [9350/10000], Loss: 1764180992.0000
Epoch [9360/10000], Loss: 1750164096.0000
Epoch [9370/10000], Loss: 1762139904.0000
Epoch [9380/10000], Loss: 1759090688.0000
Epoch [9390/10000], Loss: 1771548800.0000
Epoch [9400/10000], Loss: 1757949184.0000
Epoch [9410/10000], Loss: 1766415232.0000
Epoch [9420/10000], Loss: 1743226112.0000
Epoch [9430/10000], Loss: 1747860224.0000
Epoch [9440/10000], Loss: 1755789696.0000
Epoch [9450/10000], Loss: 1746394624.0000
Epoch [9460/10000], Loss: 1739603328.0000
Epoch [9470/10000], Loss: 1737912576.0000
Epoch [9480/10000], Loss: 1776872576.0000
Epoch [9490/10000], Loss: 1761099392.0000
Epoch [9500/10000], Loss: 1736221312.0000
Epoch [9510/10000], Loss: 1729684864.0000
Epoch [9520/10000], Loss: 1736658816.0000
Epoch [9530/10000], Loss: 1747777792.0000
Epoch [9540/10000], Loss: 1748763392.0000
Epoch [9550/10000], Loss: 1732537856.0000
Epoch [9560/10000], Loss: 1730911488.0000
Epoch [9570/10000], Loss: 1723521664.0000
Epoch [9580/10000], Loss: 1739661184.0000
Epoch [9590/10000], Loss: 1719809536.0000
Epoch [9600/10000], Loss: 1736199808.0000
Epoch [9610/10000], Loss: 1718024576.0000
Epoch [9620/10000], Loss: 1753310848.0000
Epoch [9630/10000], Loss: 1738170624.0000
Epoch [9640/10000], Loss: 1724782976.0000
Epoch [9650/10000], Loss: 1719633024.0000
Epoch [9660/10000], Loss: 1712000512.0000
Epoch [9670/10000], Loss: 1722330624.0000
Epoch [9680/10000], Loss: 1708906496.0000
Epoch [9690/10000], Loss: 1720066944.0000
Epoch [9700/10000], Loss: 1710928384.0000
Epoch [9710/10000], Loss: 1718964864.0000
Epoch [9720/10000], Loss: 1721799424.0000
Epoch [9730/10000], Loss: 1723724032.0000
Epoch [9740/10000], Loss: 1705203328.0000
Epoch [9750/10000], Loss: 1703469824.0000
Epoch [9760/10000], Loss: 1701836800.0000
Epoch [9770/10000], Loss: 1714315392.0000
Epoch [9780/10000], Loss: 1695794304.0000
Epoch [9790/10000], Loss: 1718911104.0000
Epoch [9800/10000], Loss: 1745809664.0000
Epoch [9810/10000], Loss: 1700119680.0000
Epoch [9820/10000], Loss: 1690125312.0000
Epoch [9830/10000], Loss: 1691023232.0000
Epoch [9840/10000], Loss: 1693270912.0000
Epoch [9850/10000], Loss: 1694837120.0000
Epoch [9860/10000], Loss: 1698649600.0000
Epoch [9870/10000], Loss: 1694360064.0000
Epoch [9880/10000], Loss: 1691333120.0000
Epoch [9890/10000], Loss: 1700033408.0000
Epoch [9900/10000], Loss: 1694980608.0000
Epoch [9910/10000], Loss: 1682358400.0000
Epoch [9920/10000], Loss: 1700345088.0000
Epoch [9930/10000], Loss: 1684906368.0000
Epoch [9940/10000], Loss: 1689115776.0000
Epoch [9950/10000], Loss: 1684757888.0000
Epoch [9960/10000], Loss: 1678440704.0000
Epoch [9970/10000], Loss: 1704780928.0000
Epoch [9980/10000], Loss: 1678669952.0000
Epoch [9990/10000], Loss: 1676608896.0000
Epoch [10000/10000], Loss: 1678169216.0000
Test Loss: 23384637440.0000
Training time: 23.09 seconds


#Trimming it down to what we know is relevant
Y = data['price']
X = data.drop(['id', 'date', 'price','condition', 'yr_built', 'yr_renovated', 'lat', 'long','house_age','living percentage','price_per_sqft_living', 'price_per_sqft_lot', 'years_since_renovation', 'sqft_living15', 'sqft_lot15', 'sqft_lot', 'Season'], axis=1)
random_state = 42

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state = random_state, shuffle = True)
print(X_train.columns)

Index(['bedrooms', 'bathrooms', 'sqft_living', 'floors', 'waterfront', 'view',
       'grade', 'sqft_above', 'sqft_basement', 'zipcode', 'num_rooms'],
      dtype='object')


#Converting to numpy arrays
X_train = X_train.values
X_test = X_test.values
Y_train = Y_train.values
Y_test = Y_test.values

#Normalizing features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

#Converting to PyTorch tensors
X_train = torch.tensor(X_train, dtype=torch.float32).to(device)
X_test = torch.tensor(X_test, dtype=torch.float32).to(device)
Y_train = torch.tensor(Y_train, dtype=torch.float32).view(-1, 1).to(device)
Y_test = torch.tensor(Y_test, dtype=torch.float32).view(-1, 1).to(device)

start_time = time.time()

class HousePricePredictor(nn.Module):
    def __init__(self, input_size):
        super(HousePricePredictor, self).__init__()
        self.fc0 = nn.Linear(input_size, 512)
        self.fc1 = nn.Linear(512, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 64)
        self.fc4 = nn.Linear(64, 1)
        
        
    def forward(self, x):
        x = torch.relu(self.fc0(x))
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = torch.relu(self.fc3(x))
        x = self.fc4(x)
        return x

input_size = X_train.shape[1]
model = HousePricePredictor(input_size).to(device)

#training loop
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
train_losses = []
num_epochs = 10000
for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    outputs = model(X_train)
    loss = criterion(outputs, Y_train)
    loss.backward()
    optimizer.step()
    train_losses.append(loss.item())
    
    if (epoch+1) % 10 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

#Evaluating the model
model.eval()
with torch.no_grad():
    predictions = model(X_test)
    test_loss = criterion(predictions, Y_test)
    print(f'Test Loss: {test_loss.item():.4f}')

end_time = time.time()
training_time = end_time - start_time
print(f"Training time: {training_time:.2f} seconds")

plt.plot(range(num_epochs), train_losses, label='Training Losses')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title('Training Loss Curve')
plt.legend()
plt.show()

Epoch [10/10000], Loss: 433123753984.0000
Epoch [20/10000], Loss: 433085906944.0000
Epoch [30/10000], Loss: 432908173312.0000
Epoch [40/10000], Loss: 432290594816.0000
Epoch [50/10000], Loss: 430519877632.0000
Epoch [60/10000], Loss: 426108354560.0000
Epoch [70/10000], Loss: 416304168960.0000
Epoch [80/10000], Loss: 396611158016.0000
Epoch [90/10000], Loss: 360817459200.0000
Epoch [100/10000], Loss: 302777106432.0000
Epoch [110/10000], Loss: 222353539072.0000
Epoch [120/10000], Loss: 137843884032.0000
Epoch [130/10000], Loss: 92328861696.0000
Epoch [140/10000], Loss: 91405885440.0000
Epoch [150/10000], Loss: 85511929856.0000
Epoch [160/10000], Loss: 81505902592.0000
Epoch [170/10000], Loss: 78983544832.0000
Epoch [180/10000], Loss: 76222545920.0000
Epoch [190/10000], Loss: 73866297344.0000
Epoch [200/10000], Loss: 71644569600.0000
Epoch [210/10000], Loss: 69584281600.0000
Epoch [220/10000], Loss: 67693244416.0000
Epoch [230/10000], Loss: 65963782144.0000
Epoch [240/10000], Loss: 64397950976.0000
Epoch [250/10000], Loss: 62984855552.0000
Epoch [260/10000], Loss: 61716807680.0000
Epoch [270/10000], Loss: 60585517056.0000
Epoch [280/10000], Loss: 59581640704.0000
Epoch [290/10000], Loss: 58691244032.0000
Epoch [300/10000], Loss: 57902153728.0000
Epoch [310/10000], Loss: 57202495488.0000
Epoch [320/10000], Loss: 56581398528.0000
Epoch [330/10000], Loss: 56030064640.0000
Epoch [340/10000], Loss: 55539490816.0000
Epoch [350/10000], Loss: 55101911040.0000
Epoch [360/10000], Loss: 54709227520.0000
Epoch [370/10000], Loss: 54354247680.0000
Epoch [380/10000], Loss: 54032269312.0000
Epoch [390/10000], Loss: 53738221568.0000
Epoch [400/10000], Loss: 53469122560.0000
Epoch [410/10000], Loss: 53221863424.0000
Epoch [420/10000], Loss: 52992192512.0000
Epoch [430/10000], Loss: 52778631168.0000
Epoch [440/10000], Loss: 52579926016.0000
Epoch [450/10000], Loss: 52393545728.0000
Epoch [460/10000], Loss: 52218216448.0000
Epoch [470/10000], Loss: 52053381120.0000
Epoch [480/10000], Loss: 51897520128.0000
Epoch [490/10000], Loss: 51749773312.0000
Epoch [500/10000], Loss: 51609300992.0000
Epoch [510/10000], Loss: 51475271680.0000
Epoch [520/10000], Loss: 51347415040.0000
Epoch [530/10000], Loss: 51225006080.0000
Epoch [540/10000], Loss: 51106701312.0000
Epoch [550/10000], Loss: 50992160768.0000
Epoch [560/10000], Loss: 50881916928.0000
Epoch [570/10000], Loss: 50775461888.0000
Epoch [580/10000], Loss: 50672455680.0000
Epoch [590/10000], Loss: 50573135872.0000
Epoch [600/10000], Loss: 50476826624.0000
Epoch [610/10000], Loss: 50383405056.0000
Epoch [620/10000], Loss: 50292948992.0000
Epoch [630/10000], Loss: 50205278208.0000
Epoch [640/10000], Loss: 50120241152.0000
Epoch [650/10000], Loss: 50038005760.0000
Epoch [660/10000], Loss: 49958199296.0000
Epoch [670/10000], Loss: 49880924160.0000
Epoch [680/10000], Loss: 49806135296.0000
Epoch [690/10000], Loss: 49733861376.0000
Epoch [700/10000], Loss: 49663827968.0000
Epoch [710/10000], Loss: 49595822080.0000
Epoch [720/10000], Loss: 49529806848.0000
Epoch [730/10000], Loss: 49465511936.0000
Epoch [740/10000], Loss: 49402994688.0000
Epoch [750/10000], Loss: 49342222336.0000
Epoch [760/10000], Loss: 49282969600.0000
Epoch [770/10000], Loss: 49225150464.0000
Epoch [780/10000], Loss: 49168609280.0000
Epoch [790/10000], Loss: 49113288704.0000
Epoch [800/10000], Loss: 49059307520.0000
Epoch [810/10000], Loss: 49006460928.0000
Epoch [820/10000], Loss: 48954724352.0000
Epoch [830/10000], Loss: 48903892992.0000
Epoch [840/10000], Loss: 48853954560.0000
Epoch [850/10000], Loss: 48805195776.0000
Epoch [860/10000], Loss: 48757596160.0000
Epoch [870/10000], Loss: 48710979584.0000
Epoch [880/10000], Loss: 48665198592.0000
Epoch [890/10000], Loss: 48620236800.0000
Epoch [900/10000], Loss: 48575926272.0000
Epoch [910/10000], Loss: 48531972096.0000
Epoch [920/10000], Loss: 48488517632.0000
Epoch [930/10000], Loss: 48445558784.0000
Epoch [940/10000], Loss: 48403062784.0000
Epoch [950/10000], Loss: 48361123840.0000
Epoch [960/10000], Loss: 48319639552.0000
Epoch [970/10000], Loss: 48278495232.0000
Epoch [980/10000], Loss: 48237621248.0000
Epoch [990/10000], Loss: 48196780032.0000
Epoch [1000/10000], Loss: 48156098560.0000
Epoch [1010/10000], Loss: 48115376128.0000
Epoch [1020/10000], Loss: 48074670080.0000
Epoch [1030/10000], Loss: 48034029568.0000
Epoch [1040/10000], Loss: 47993475072.0000
Epoch [1050/10000], Loss: 47952949248.0000
Epoch [1060/10000], Loss: 47912316928.0000
Epoch [1070/10000], Loss: 47871500288.0000
Epoch [1080/10000], Loss: 47830519808.0000
Epoch [1090/10000], Loss: 47789236224.0000
Epoch [1100/10000], Loss: 47747649536.0000
Epoch [1110/10000], Loss: 47705694208.0000
Epoch [1120/10000], Loss: 47663353856.0000
Epoch [1130/10000], Loss: 47620759552.0000
Epoch [1140/10000], Loss: 47577899008.0000
Epoch [1150/10000], Loss: 47534641152.0000
Epoch [1160/10000], Loss: 47490953216.0000
Epoch [1170/10000], Loss: 47446777856.0000
Epoch [1180/10000], Loss: 47402016768.0000
Epoch [1190/10000], Loss: 47356772352.0000
Epoch [1200/10000], Loss: 47310921728.0000
Epoch [1210/10000], Loss: 47264571392.0000
Epoch [1220/10000], Loss: 47217758208.0000
Epoch [1230/10000], Loss: 47170416640.0000
Epoch [1240/10000], Loss: 47122608128.0000
Epoch [1250/10000], Loss: 47074250752.0000
Epoch [1260/10000], Loss: 47025512448.0000
Epoch [1270/10000], Loss: 46976401408.0000
Epoch [1280/10000], Loss: 46927015936.0000
Epoch [1290/10000], Loss: 46877319168.0000
Epoch [1300/10000], Loss: 46827229184.0000
Epoch [1310/10000], Loss: 46776819712.0000
Epoch [1320/10000], Loss: 46726045696.0000
Epoch [1330/10000], Loss: 46674935808.0000
Epoch [1340/10000], Loss: 46623571968.0000
Epoch [1350/10000], Loss: 46571896832.0000
Epoch [1360/10000], Loss: 46520197120.0000
Epoch [1370/10000], Loss: 46468505600.0000
Epoch [1380/10000], Loss: 46416666624.0000
Epoch [1390/10000], Loss: 46364721152.0000
Epoch [1400/10000], Loss: 46312660992.0000
Epoch [1410/10000], Loss: 46260436992.0000
Epoch [1420/10000], Loss: 46207950848.0000
Epoch [1430/10000], Loss: 46155329536.0000
Epoch [1440/10000], Loss: 46102659072.0000
Epoch [1450/10000], Loss: 46049714176.0000
Epoch [1460/10000], Loss: 45996560384.0000
Epoch [1470/10000], Loss: 45943492608.0000
Epoch [1480/10000], Loss: 45890469888.0000
Epoch [1490/10000], Loss: 45837217792.0000
Epoch [1500/10000], Loss: 45783986176.0000
Epoch [1510/10000], Loss: 45730725888.0000
Epoch [1520/10000], Loss: 45677473792.0000
Epoch [1530/10000], Loss: 45624205312.0000
Epoch [1540/10000], Loss: 45570584576.0000
Epoch [1550/10000], Loss: 45516656640.0000
Epoch [1560/10000], Loss: 45462568960.0000
Epoch [1570/10000], Loss: 45408366592.0000
Epoch [1580/10000], Loss: 45354106880.0000
Epoch [1590/10000], Loss: 45299748864.0000
Epoch [1600/10000], Loss: 45245194240.0000
Epoch [1610/10000], Loss: 45190438912.0000
Epoch [1620/10000], Loss: 45135618048.0000
Epoch [1630/10000], Loss: 45080973312.0000
Epoch [1640/10000], Loss: 45026299904.0000
Epoch [1650/10000], Loss: 44971798528.0000
Epoch [1660/10000], Loss: 44917346304.0000
Epoch [1670/10000], Loss: 44862742528.0000
Epoch [1680/10000], Loss: 44808183808.0000
Epoch [1690/10000], Loss: 44753489920.0000
Epoch [1700/10000], Loss: 44698550272.0000
Epoch [1710/10000], Loss: 44643352576.0000
Epoch [1720/10000], Loss: 44588212224.0000
Epoch [1730/10000], Loss: 44532850688.0000
Epoch [1740/10000], Loss: 44476874752.0000
Epoch [1750/10000], Loss: 44420567040.0000
Epoch [1760/10000], Loss: 44365099008.0000
Epoch [1770/10000], Loss: 44309479424.0000
Epoch [1780/10000], Loss: 44253646848.0000
Epoch [1790/10000], Loss: 44197101568.0000
Epoch [1800/10000], Loss: 44139503616.0000
Epoch [1810/10000], Loss: 44081954816.0000
Epoch [1820/10000], Loss: 44024471552.0000
Epoch [1830/10000], Loss: 43966869504.0000
Epoch [1840/10000], Loss: 43908521984.0000
Epoch [1850/10000], Loss: 43849953280.0000
Epoch [1860/10000], Loss: 43791216640.0000
Epoch [1870/10000], Loss: 43732287488.0000
Epoch [1880/10000], Loss: 43673018368.0000
Epoch [1890/10000], Loss: 43613114368.0000
Epoch [1900/10000], Loss: 43552575488.0000
Epoch [1910/10000], Loss: 43491168256.0000
Epoch [1920/10000], Loss: 43428966400.0000
Epoch [1930/10000], Loss: 43366498304.0000
Epoch [1940/10000], Loss: 43305971712.0000
Epoch [1950/10000], Loss: 43244978176.0000
Epoch [1960/10000], Loss: 43183398912.0000
Epoch [1970/10000], Loss: 43121479680.0000
Epoch [1980/10000], Loss: 43058610176.0000
Epoch [1990/10000], Loss: 42994343936.0000
Epoch [2000/10000], Loss: 42928746496.0000
Epoch [2010/10000], Loss: 42863177728.0000
Epoch [2020/10000], Loss: 42798022656.0000
Epoch [2030/10000], Loss: 42732965888.0000
Epoch [2040/10000], Loss: 42666950656.0000
Epoch [2050/10000], Loss: 42601414656.0000
Epoch [2060/10000], Loss: 42535682048.0000
Epoch [2070/10000], Loss: 42469240832.0000
Epoch [2080/10000], Loss: 42403065856.0000
Epoch [2090/10000], Loss: 42337288192.0000
Epoch [2100/10000], Loss: 42271580160.0000
Epoch [2110/10000], Loss: 42205184000.0000
Epoch [2120/10000], Loss: 42138931200.0000
Epoch [2130/10000], Loss: 42071752704.0000
Epoch [2140/10000], Loss: 42003578880.0000
Epoch [2150/10000], Loss: 41934082048.0000
Epoch [2160/10000], Loss: 41862565888.0000
Epoch [2170/10000], Loss: 41791700992.0000
Epoch [2180/10000], Loss: 41721675776.0000
Epoch [2190/10000], Loss: 41651695616.0000
Epoch [2200/10000], Loss: 41581367296.0000
Epoch [2210/10000], Loss: 41511755776.0000
Epoch [2220/10000], Loss: 41443069952.0000
Epoch [2230/10000], Loss: 41375137792.0000
Epoch [2240/10000], Loss: 41309499392.0000
Epoch [2250/10000], Loss: 41245118464.0000
Epoch [2260/10000], Loss: 41182224384.0000
Epoch [2270/10000], Loss: 41119899648.0000
Epoch [2280/10000], Loss: 41058168832.0000
Epoch [2290/10000], Loss: 40997515264.0000
Epoch [2300/10000], Loss: 40937549824.0000
Epoch [2310/10000], Loss: 40877572096.0000
Epoch [2320/10000], Loss: 40817364992.0000
Epoch [2330/10000], Loss: 40756547584.0000
Epoch [2340/10000], Loss: 40695144448.0000
Epoch [2350/10000], Loss: 40633778176.0000
Epoch [2360/10000], Loss: 40571150336.0000
Epoch [2370/10000], Loss: 40508354560.0000
Epoch [2380/10000], Loss: 40446668800.0000
Epoch [2390/10000], Loss: 40385581056.0000
Epoch [2400/10000], Loss: 40325083136.0000
Epoch [2410/10000], Loss: 40263995392.0000
Epoch [2420/10000], Loss: 40202272768.0000
Epoch [2430/10000], Loss: 40141389824.0000
Epoch [2440/10000], Loss: 40079626240.0000
Epoch [2450/10000], Loss: 40017772544.0000
Epoch [2460/10000], Loss: 39954550784.0000
Epoch [2470/10000], Loss: 39891140608.0000
Epoch [2480/10000], Loss: 39827517440.0000
Epoch [2490/10000], Loss: 39763283968.0000
Epoch [2500/10000], Loss: 39697616896.0000
Epoch [2510/10000], Loss: 39632187392.0000
Epoch [2520/10000], Loss: 39567339520.0000
Epoch [2530/10000], Loss: 39501819904.0000
Epoch [2540/10000], Loss: 39436402688.0000
Epoch [2550/10000], Loss: 39370784768.0000
Epoch [2560/10000], Loss: 39306240000.0000
Epoch [2570/10000], Loss: 39243235328.0000
Epoch [2580/10000], Loss: 39180664832.0000
Epoch [2590/10000], Loss: 39117889536.0000
Epoch [2600/10000], Loss: 39055564800.0000
Epoch [2610/10000], Loss: 38993301504.0000
Epoch [2620/10000], Loss: 38931697664.0000
Epoch [2630/10000], Loss: 38869135360.0000
Epoch [2640/10000], Loss: 38807162880.0000
Epoch [2650/10000], Loss: 38746361856.0000
Epoch [2660/10000], Loss: 38685904896.0000
Epoch [2670/10000], Loss: 38625583104.0000
Epoch [2680/10000], Loss: 38564761600.0000
Epoch [2690/10000], Loss: 38502772736.0000
Epoch [2700/10000], Loss: 38440820736.0000
Epoch [2710/10000], Loss: 38378803200.0000
Epoch [2720/10000], Loss: 38316101632.0000
Epoch [2730/10000], Loss: 38252744704.0000
Epoch [2740/10000], Loss: 38189580288.0000
Epoch [2750/10000], Loss: 38126247936.0000
Epoch [2760/10000], Loss: 38062063616.0000
Epoch [2770/10000], Loss: 37998301184.0000
Epoch [2780/10000], Loss: 37935026176.0000
Epoch [2790/10000], Loss: 37870981120.0000
Epoch [2800/10000], Loss: 37807476736.0000
Epoch [2810/10000], Loss: 37744279552.0000
Epoch [2820/10000], Loss: 37681082368.0000
Epoch [2830/10000], Loss: 37617844224.0000
Epoch [2840/10000], Loss: 37554446336.0000
Epoch [2850/10000], Loss: 37491601408.0000
Epoch [2860/10000], Loss: 37429387264.0000
Epoch [2870/10000], Loss: 37367332864.0000
Epoch [2880/10000], Loss: 37304868864.0000
Epoch [2890/10000], Loss: 37243310080.0000
Epoch [2900/10000], Loss: 37182840832.0000
Epoch [2910/10000], Loss: 37120557056.0000
Epoch [2920/10000], Loss: 37057081344.0000
Epoch [2930/10000], Loss: 36993789952.0000
Epoch [2940/10000], Loss: 36931321856.0000
Epoch [2950/10000], Loss: 36870520832.0000
Epoch [2960/10000], Loss: 36808220672.0000
Epoch [2970/10000], Loss: 36745846784.0000
Epoch [2980/10000], Loss: 36683567104.0000
Epoch [2990/10000], Loss: 36621447168.0000
Epoch [3000/10000], Loss: 36558647296.0000
Epoch [3010/10000], Loss: 36495294464.0000
Epoch [3020/10000], Loss: 36430897152.0000
Epoch [3030/10000], Loss: 36365135872.0000
Epoch [3040/10000], Loss: 36299333632.0000
Epoch [3050/10000], Loss: 36233932800.0000
Epoch [3060/10000], Loss: 36168835072.0000
Epoch [3070/10000], Loss: 36103720960.0000
Epoch [3080/10000], Loss: 36038590464.0000
Epoch [3090/10000], Loss: 35972071424.0000
Epoch [3100/10000], Loss: 35903279104.0000
Epoch [3110/10000], Loss: 35831713792.0000
Epoch [3120/10000], Loss: 35760906240.0000
Epoch [3130/10000], Loss: 35690352640.0000
Epoch [3140/10000], Loss: 35623174144.0000
Epoch [3150/10000], Loss: 35557777408.0000
Epoch [3160/10000], Loss: 35492593664.0000
Epoch [3170/10000], Loss: 35429822464.0000
Epoch [3180/10000], Loss: 35368087552.0000
Epoch [3190/10000], Loss: 35304632320.0000
Epoch [3200/10000], Loss: 35239518208.0000
Epoch [3210/10000], Loss: 35175227392.0000
Epoch [3220/10000], Loss: 35111505920.0000
Epoch [3230/10000], Loss: 35048902656.0000
Epoch [3240/10000], Loss: 34986418176.0000
Epoch [3250/10000], Loss: 34924318720.0000
Epoch [3260/10000], Loss: 34864910336.0000
Epoch [3270/10000], Loss: 34805055488.0000
Epoch [3280/10000], Loss: 34743554048.0000
Epoch [3290/10000], Loss: 34681892864.0000
Epoch [3300/10000], Loss: 34618687488.0000
Epoch [3310/10000], Loss: 34556387328.0000
Epoch [3320/10000], Loss: 34497523712.0000
Epoch [3330/10000], Loss: 34439299072.0000
Epoch [3340/10000], Loss: 34380333056.0000
Epoch [3350/10000], Loss: 34319366144.0000
Epoch [3360/10000], Loss: 34259963904.0000
Epoch [3370/10000], Loss: 34201683968.0000
Epoch [3380/10000], Loss: 34144407552.0000
Epoch [3390/10000], Loss: 34088597504.0000
Epoch [3400/10000], Loss: 34032793600.0000
Epoch [3410/10000], Loss: 33975797760.0000
Epoch [3420/10000], Loss: 33919277056.0000
Epoch [3430/10000], Loss: 33862860800.0000
Epoch [3440/10000], Loss: 33807140864.0000
Epoch [3450/10000], Loss: 33751066624.0000
Epoch [3460/10000], Loss: 33694214144.0000
Epoch [3470/10000], Loss: 33636380672.0000
Epoch [3480/10000], Loss: 33579370496.0000
Epoch [3490/10000], Loss: 33520928768.0000
Epoch [3500/10000], Loss: 33462120448.0000
Epoch [3510/10000], Loss: 33401196544.0000
Epoch [3520/10000], Loss: 33339387904.0000
Epoch [3530/10000], Loss: 33278212096.0000
Epoch [3540/10000], Loss: 33218537472.0000
Epoch [3550/10000], Loss: 33159372800.0000
Epoch [3560/10000], Loss: 33101731840.0000
Epoch [3570/10000], Loss: 33043058688.0000
Epoch [3580/10000], Loss: 32982636544.0000
Epoch [3590/10000], Loss: 32921874432.0000
Epoch [3600/10000], Loss: 32861376512.0000
Epoch [3610/10000], Loss: 32797163520.0000
Epoch [3620/10000], Loss: 32736786432.0000
Epoch [3630/10000], Loss: 32677056512.0000
Epoch [3640/10000], Loss: 32617498624.0000
Epoch [3650/10000], Loss: 32556343296.0000
Epoch [3660/10000], Loss: 32496867328.0000
Epoch [3670/10000], Loss: 32434368512.0000
Epoch [3680/10000], Loss: 32375412736.0000
Epoch [3690/10000], Loss: 32316981248.0000
Epoch [3700/10000], Loss: 32256516096.0000
Epoch [3710/10000], Loss: 32193832960.0000
Epoch [3720/10000], Loss: 32137531392.0000
Epoch [3730/10000], Loss: 32075558912.0000
Epoch [3740/10000], Loss: 32009701376.0000
Epoch [3750/10000], Loss: 31946993664.0000
Epoch [3760/10000], Loss: 31891560448.0000
Epoch [3770/10000], Loss: 31835664384.0000
Epoch [3780/10000], Loss: 31774582784.0000
Epoch [3790/10000], Loss: 31715045376.0000
Epoch [3800/10000], Loss: 31661099008.0000
Epoch [3810/10000], Loss: 31604490240.0000
Epoch [3820/10000], Loss: 31540146176.0000
Epoch [3830/10000], Loss: 31480018944.0000
Epoch [3840/10000], Loss: 31423313920.0000
Epoch [3850/10000], Loss: 31360931840.0000
Epoch [3860/10000], Loss: 31293685760.0000
Epoch [3870/10000], Loss: 31236620288.0000
Epoch [3880/10000], Loss: 31176226816.0000
Epoch [3890/10000], Loss: 31112638464.0000
Epoch [3900/10000], Loss: 31065583616.0000
Epoch [3910/10000], Loss: 31001249792.0000
Epoch [3920/10000], Loss: 30939500544.0000
Epoch [3930/10000], Loss: 30876381184.0000
Epoch [3940/10000], Loss: 30812821504.0000
Epoch [3950/10000], Loss: 30752745472.0000
Epoch [3960/10000], Loss: 30698539008.0000
Epoch [3970/10000], Loss: 30631655424.0000
Epoch [3980/10000], Loss: 30567356416.0000
Epoch [3990/10000], Loss: 30507431936.0000
Epoch [4000/10000], Loss: 30456631296.0000
Epoch [4010/10000], Loss: 30389622784.0000
Epoch [4020/10000], Loss: 30322411520.0000
Epoch [4030/10000], Loss: 30277322752.0000
Epoch [4040/10000], Loss: 30211627008.0000
Epoch [4050/10000], Loss: 30141956096.0000
Epoch [4060/10000], Loss: 30083686400.0000
Epoch [4070/10000], Loss: 30024067072.0000
Epoch [4080/10000], Loss: 29963767808.0000
Epoch [4090/10000], Loss: 29893201920.0000
Epoch [4100/10000], Loss: 29829593088.0000
Epoch [4110/10000], Loss: 29776230400.0000
Epoch [4120/10000], Loss: 29719533568.0000
Epoch [4130/10000], Loss: 29653594112.0000
Epoch [4140/10000], Loss: 29584039936.0000
Epoch [4150/10000], Loss: 29523994624.0000
Epoch [4160/10000], Loss: 29469421568.0000
Epoch [4170/10000], Loss: 29409118208.0000
Epoch [4180/10000], Loss: 29336248320.0000
Epoch [4190/10000], Loss: 29269356544.0000
Epoch [4200/10000], Loss: 29212248064.0000
Epoch [4210/10000], Loss: 29156270080.0000
Epoch [4220/10000], Loss: 29085296640.0000
Epoch [4230/10000], Loss: 29024149504.0000
Epoch [4240/10000], Loss: 28974002176.0000
Epoch [4250/10000], Loss: 28925597696.0000
Epoch [4260/10000], Loss: 28864735232.0000
Epoch [4270/10000], Loss: 28797915136.0000
Epoch [4280/10000], Loss: 28733919232.0000
Epoch [4290/10000], Loss: 28697649152.0000
Epoch [4300/10000], Loss: 28636053504.0000
Epoch [4310/10000], Loss: 28572121088.0000
Epoch [4320/10000], Loss: 28507367424.0000
Epoch [4330/10000], Loss: 28454770688.0000
Epoch [4340/10000], Loss: 28409169920.0000
Epoch [4350/10000], Loss: 28340058112.0000
Epoch [4360/10000], Loss: 28274032640.0000
Epoch [4370/10000], Loss: 28232183808.0000
Epoch [4380/10000], Loss: 28180299776.0000
Epoch [4390/10000], Loss: 28109682688.0000
Epoch [4400/10000], Loss: 28048709632.0000
Epoch [4410/10000], Loss: 28001058816.0000
Epoch [4420/10000], Loss: 27958376448.0000
Epoch [4430/10000], Loss: 27908364288.0000
Epoch [4440/10000], Loss: 27848321024.0000
Epoch [4450/10000], Loss: 27789520896.0000
Epoch [4460/10000], Loss: 27734818816.0000
Epoch [4470/10000], Loss: 27684968448.0000
Epoch [4480/10000], Loss: 27656052736.0000
Epoch [4490/10000], Loss: 27598372864.0000
Epoch [4500/10000], Loss: 27539243008.0000
Epoch [4510/10000], Loss: 27480328192.0000
Epoch [4520/10000], Loss: 27434561536.0000
Epoch [4530/10000], Loss: 27393921024.0000
Epoch [4540/10000], Loss: 27355652096.0000
Epoch [4550/10000], Loss: 27295070208.0000
Epoch [4560/10000], Loss: 27239047168.0000
Epoch [4570/10000], Loss: 27186755584.0000
Epoch [4580/10000], Loss: 27148347392.0000
Epoch [4590/10000], Loss: 27109529600.0000
Epoch [4600/10000], Loss: 27064543232.0000
Epoch [4610/10000], Loss: 27008882688.0000
Epoch [4620/10000], Loss: 26949349376.0000
Epoch [4630/10000], Loss: 26898837504.0000
Epoch [4640/10000], Loss: 26867986432.0000
Epoch [4650/10000], Loss: 26833250304.0000
Epoch [4660/10000], Loss: 26776686592.0000
Epoch [4670/10000], Loss: 26717870080.0000
Epoch [4680/10000], Loss: 26670292992.0000
Epoch [4690/10000], Loss: 26643503104.0000
Epoch [4700/10000], Loss: 26604355584.0000
Epoch [4710/10000], Loss: 26548557824.0000
Epoch [4720/10000], Loss: 26485551104.0000
Epoch [4730/10000], Loss: 26433857536.0000
Epoch [4740/10000], Loss: 26397999104.0000
Epoch [4750/10000], Loss: 26370013184.0000
Epoch [4760/10000], Loss: 26320424960.0000
Epoch [4770/10000], Loss: 26262077440.0000
Epoch [4780/10000], Loss: 26210578432.0000
Epoch [4790/10000], Loss: 26166820864.0000
Epoch [4800/10000], Loss: 26157262848.0000
Epoch [4810/10000], Loss: 26109483008.0000
Epoch [4820/10000], Loss: 26050703360.0000
Epoch [4830/10000], Loss: 25996228608.0000
Epoch [4840/10000], Loss: 25972238336.0000
Epoch [4850/10000], Loss: 25938556928.0000
Epoch [4860/10000], Loss: 25878867968.0000
Epoch [4870/10000], Loss: 25826349056.0000
Epoch [4880/10000], Loss: 25803890688.0000
Epoch [4890/10000], Loss: 25777180672.0000
Epoch [4900/10000], Loss: 25723641856.0000
Epoch [4910/10000], Loss: 25667381248.0000
Epoch [4920/10000], Loss: 25623672832.0000
Epoch [4930/10000], Loss: 25594804224.0000
Epoch [4940/10000], Loss: 25577113600.0000
Epoch [4950/10000], Loss: 25536501760.0000
Epoch [4960/10000], Loss: 25483905024.0000
Epoch [4970/10000], Loss: 25432236032.0000
Epoch [4980/10000], Loss: 25400788992.0000
Epoch [4990/10000], Loss: 25399463936.0000
Epoch [5000/10000], Loss: 25354465280.0000
Epoch [5010/10000], Loss: 25298753536.0000
Epoch [5020/10000], Loss: 25250152448.0000
Epoch [5030/10000], Loss: 25219721216.0000
Epoch [5040/10000], Loss: 25206489088.0000
Epoch [5050/10000], Loss: 25181310976.0000
Epoch [5060/10000], Loss: 25143289856.0000
Epoch [5070/10000], Loss: 25105645568.0000
Epoch [5080/10000], Loss: 25065107456.0000
Epoch [5090/10000], Loss: 25017317376.0000
Epoch [5100/10000], Loss: 24972283904.0000
Epoch [5110/10000], Loss: 24934078464.0000
Epoch [5120/10000], Loss: 24906354688.0000
Epoch [5130/10000], Loss: 24890351616.0000
Epoch [5140/10000], Loss: 24861689856.0000
Epoch [5150/10000], Loss: 24822073344.0000
Epoch [5160/10000], Loss: 24769449984.0000
Epoch [5170/10000], Loss: 24722096128.0000
Epoch [5180/10000], Loss: 24680411136.0000
Epoch [5190/10000], Loss: 24644280320.0000
Epoch [5200/10000], Loss: 24640305152.0000
Epoch [5210/10000], Loss: 24611229696.0000
Epoch [5220/10000], Loss: 24576212992.0000
Epoch [5230/10000], Loss: 24525670400.0000
Epoch [5240/10000], Loss: 24477536256.0000
Epoch [5250/10000], Loss: 24428652544.0000
Epoch [5260/10000], Loss: 24393379840.0000
Epoch [5270/10000], Loss: 24374663168.0000
Epoch [5280/10000], Loss: 24358023168.0000
Epoch [5290/10000], Loss: 24311474176.0000
Epoch [5300/10000], Loss: 24251467776.0000
Epoch [5310/10000], Loss: 24207544320.0000
Epoch [5320/10000], Loss: 24184920064.0000
Epoch [5330/10000], Loss: 24181473280.0000
Epoch [5340/10000], Loss: 24149334016.0000
Epoch [5350/10000], Loss: 24094713856.0000
Epoch [5360/10000], Loss: 24038144000.0000
Epoch [5370/10000], Loss: 24008751104.0000
Epoch [5380/10000], Loss: 24007067648.0000
Epoch [5390/10000], Loss: 23989022720.0000
Epoch [5400/10000], Loss: 23936137216.0000
Epoch [5410/10000], Loss: 23888076800.0000
Epoch [5420/10000], Loss: 23839557632.0000
Epoch [5430/10000], Loss: 23802568704.0000
Epoch [5440/10000], Loss: 23784148992.0000
Epoch [5450/10000], Loss: 23778496512.0000
Epoch [5460/10000], Loss: 23737868288.0000
Epoch [5470/10000], Loss: 23682394112.0000
Epoch [5480/10000], Loss: 23641133056.0000
Epoch [5490/10000], Loss: 23635167232.0000
Epoch [5500/10000], Loss: 23615313920.0000
Epoch [5510/10000], Loss: 23576317952.0000
Epoch [5520/10000], Loss: 23510163456.0000
Epoch [5530/10000], Loss: 23474604032.0000
Epoch [5540/10000], Loss: 23489828864.0000
Epoch [5550/10000], Loss: 23448805376.0000
Epoch [5560/10000], Loss: 23392118784.0000
Epoch [5570/10000], Loss: 23345240064.0000
Epoch [5580/10000], Loss: 23338510336.0000
Epoch [5590/10000], Loss: 23346452480.0000
Epoch [5600/10000], Loss: 23292571648.0000
Epoch [5610/10000], Loss: 23244279808.0000
Epoch [5620/10000], Loss: 23198699520.0000
Epoch [5630/10000], Loss: 23177787392.0000
Epoch [5640/10000], Loss: 23179061248.0000
Epoch [5650/10000], Loss: 23157673984.0000
Epoch [5660/10000], Loss: 23101814784.0000
Epoch [5670/10000], Loss: 23057065984.0000
Epoch [5680/10000], Loss: 23042592768.0000
Epoch [5690/10000], Loss: 23030523904.0000
Epoch [5700/10000], Loss: 23006332928.0000
Epoch [5710/10000], Loss: 22985975808.0000
Epoch [5720/10000], Loss: 22942398464.0000
Epoch [5730/10000], Loss: 22890737664.0000
Epoch [5740/10000], Loss: 22862981120.0000
Epoch [5750/10000], Loss: 22876276736.0000
Epoch [5760/10000], Loss: 22856278016.0000
Epoch [5770/10000], Loss: 22817579008.0000
Epoch [5780/10000], Loss: 22786762752.0000
Epoch [5790/10000], Loss: 22741630976.0000
Epoch [5800/10000], Loss: 22696540160.0000
Epoch [5810/10000], Loss: 22655295488.0000
Epoch [5820/10000], Loss: 22647140352.0000
Epoch [5830/10000], Loss: 22642028544.0000
Epoch [5840/10000], Loss: 22609741824.0000
Epoch [5850/10000], Loss: 22594662400.0000
Epoch [5860/10000], Loss: 22555930624.0000
Epoch [5870/10000], Loss: 22519951360.0000
Epoch [5880/10000], Loss: 22462175232.0000
Epoch [5890/10000], Loss: 22442526720.0000
Epoch [5900/10000], Loss: 22455840768.0000
Epoch [5910/10000], Loss: 22420537344.0000
Epoch [5920/10000], Loss: 22374062080.0000
Epoch [5930/10000], Loss: 22322110464.0000
Epoch [5940/10000], Loss: 22295185408.0000
Epoch [5950/10000], Loss: 22311811072.0000
Epoch [5960/10000], Loss: 22297444352.0000
Epoch [5970/10000], Loss: 22250579968.0000
Epoch [5980/10000], Loss: 22194055168.0000
Epoch [5990/10000], Loss: 22153986048.0000
Epoch [6000/10000], Loss: 22153705472.0000
Epoch [6010/10000], Loss: 22156886016.0000
Epoch [6020/10000], Loss: 22102487040.0000
Epoch [6030/10000], Loss: 22055790592.0000
Epoch [6040/10000], Loss: 22018041856.0000
Epoch [6050/10000], Loss: 21989267456.0000
Epoch [6060/10000], Loss: 21983262720.0000
Epoch [6070/10000], Loss: 22000551936.0000
Epoch [6080/10000], Loss: 21943613440.0000
Epoch [6090/10000], Loss: 21884358656.0000
Epoch [6100/10000], Loss: 21851467776.0000
Epoch [6110/10000], Loss: 21861875712.0000
Epoch [6120/10000], Loss: 21857095680.0000
Epoch [6130/10000], Loss: 21820035072.0000
Epoch [6140/10000], Loss: 21757509632.0000
Epoch [6150/10000], Loss: 21715494912.0000
Epoch [6160/10000], Loss: 21709869056.0000
Epoch [6170/10000], Loss: 21712889856.0000
Epoch [6180/10000], Loss: 21690556416.0000
Epoch [6190/10000], Loss: 21640290304.0000
Epoch [6200/10000], Loss: 21584900096.0000
Epoch [6210/10000], Loss: 21557377024.0000
Epoch [6220/10000], Loss: 21553883136.0000
Epoch [6230/10000], Loss: 21552515072.0000
Epoch [6240/10000], Loss: 21537478656.0000
Epoch [6250/10000], Loss: 21478666240.0000
Epoch [6260/10000], Loss: 21425152000.0000
Epoch [6270/10000], Loss: 21430923264.0000
Epoch [6280/10000], Loss: 21445394432.0000
Epoch [6290/10000], Loss: 21419790336.0000
Epoch [6300/10000], Loss: 21365147648.0000
Epoch [6310/10000], Loss: 21307983872.0000
Epoch [6320/10000], Loss: 21264586752.0000
Epoch [6330/10000], Loss: 21295435776.0000
Epoch [6340/10000], Loss: 21276887040.0000
Epoch [6350/10000], Loss: 21208651776.0000
Epoch [6360/10000], Loss: 21159395328.0000
Epoch [6370/10000], Loss: 21134372864.0000
Epoch [6380/10000], Loss: 21152436224.0000
Epoch [6390/10000], Loss: 21161320448.0000
Epoch [6400/10000], Loss: 21102381056.0000
Epoch [6410/10000], Loss: 21041113088.0000
Epoch [6420/10000], Loss: 21004853248.0000
Epoch [6430/10000], Loss: 21010671616.0000
Epoch [6440/10000], Loss: 21028050944.0000
Epoch [6450/10000], Loss: 20985217024.0000
Epoch [6460/10000], Loss: 20929265664.0000
Epoch [6470/10000], Loss: 20880529408.0000
Epoch [6480/10000], Loss: 20860753920.0000
Epoch [6490/10000], Loss: 20870588416.0000
Epoch [6500/10000], Loss: 20866107392.0000
Epoch [6510/10000], Loss: 20820973568.0000
Epoch [6520/10000], Loss: 20758581248.0000
Epoch [6530/10000], Loss: 20745345024.0000
Epoch [6540/10000], Loss: 20797857792.0000
Epoch [6550/10000], Loss: 20734986240.0000
Epoch [6560/10000], Loss: 20675155968.0000
Epoch [6570/10000], Loss: 20629929984.0000
Epoch [6580/10000], Loss: 20602953728.0000
Epoch [6590/10000], Loss: 20619286528.0000
Epoch [6600/10000], Loss: 20622215168.0000
Epoch [6610/10000], Loss: 20546328576.0000
Epoch [6620/10000], Loss: 20503025664.0000
Epoch [6630/10000], Loss: 20510912512.0000
Epoch [6640/10000], Loss: 20550158336.0000
Epoch [6650/10000], Loss: 20508598272.0000
Epoch [6660/10000], Loss: 20478111744.0000
Epoch [6670/10000], Loss: 20414164992.0000
Epoch [6680/10000], Loss: 20355600384.0000
Epoch [6690/10000], Loss: 20424904704.0000
Epoch [6700/10000], Loss: 20400816128.0000
Epoch [6710/10000], Loss: 20339173376.0000
Epoch [6720/10000], Loss: 20272113664.0000
Epoch [6730/10000], Loss: 20252700672.0000
Epoch [6740/10000], Loss: 20299364352.0000
Epoch [6750/10000], Loss: 20241811456.0000
Epoch [6760/10000], Loss: 20185722880.0000
Epoch [6770/10000], Loss: 20145645568.0000
Epoch [6780/10000], Loss: 20135620608.0000
Epoch [6790/10000], Loss: 20172238848.0000
Epoch [6800/10000], Loss: 20104075264.0000
Epoch [6810/10000], Loss: 20049655808.0000
Epoch [6820/10000], Loss: 20037027840.0000
Epoch [6830/10000], Loss: 20053979136.0000
Epoch [6840/10000], Loss: 20055525376.0000
Epoch [6850/10000], Loss: 19988193280.0000
Epoch [6860/10000], Loss: 19937982464.0000
Epoch [6870/10000], Loss: 20016496640.0000
Epoch [6880/10000], Loss: 19917635584.0000
Epoch [6890/10000], Loss: 19866652672.0000
Epoch [6900/10000], Loss: 19846780928.0000
Epoch [6910/10000], Loss: 19836766208.0000
Epoch [6920/10000], Loss: 19849043968.0000
Epoch [6930/10000], Loss: 19842002944.0000
Epoch [6940/10000], Loss: 19795740672.0000
Epoch [6950/10000], Loss: 19729156096.0000
Epoch [6960/10000], Loss: 19709251584.0000
Epoch [6970/10000], Loss: 19713462272.0000
Epoch [6980/10000], Loss: 19719387136.0000
Epoch [6990/10000], Loss: 19674273792.0000
Epoch [7000/10000], Loss: 19610474496.0000
Epoch [7010/10000], Loss: 19618813952.0000
Epoch [7020/10000], Loss: 19652276224.0000
Epoch [7030/10000], Loss: 19626444800.0000
Epoch [7040/10000], Loss: 19583145984.0000
Epoch [7050/10000], Loss: 19515967488.0000
Epoch [7060/10000], Loss: 19482669056.0000
Epoch [7070/10000], Loss: 19533185024.0000
Epoch [7080/10000], Loss: 19483314176.0000
Epoch [7090/10000], Loss: 19408732160.0000
Epoch [7100/10000], Loss: 19429699584.0000
Epoch [7110/10000], Loss: 19467163648.0000
Epoch [7120/10000], Loss: 19432777728.0000
Epoch [7130/10000], Loss: 19375972352.0000
Epoch [7140/10000], Loss: 19316619264.0000
Epoch [7150/10000], Loss: 19271487488.0000
Epoch [7160/10000], Loss: 19321278464.0000
Epoch [7170/10000], Loss: 19308621824.0000
Epoch [7180/10000], Loss: 19229022208.0000
Epoch [7190/10000], Loss: 19183927296.0000
Epoch [7200/10000], Loss: 19172554752.0000
Epoch [7210/10000], Loss: 19180204032.0000
Epoch [7220/10000], Loss: 19194804224.0000
Epoch [7230/10000], Loss: 19111843840.0000
Epoch [7240/10000], Loss: 19094339584.0000
Epoch [7250/10000], Loss: 19169361920.0000
Epoch [7260/10000], Loss: 19123433472.0000
Epoch [7270/10000], Loss: 19077253120.0000
Epoch [7280/10000], Loss: 19006824448.0000
Epoch [7290/10000], Loss: 18961547264.0000
Epoch [7300/10000], Loss: 19028400128.0000
Epoch [7310/10000], Loss: 18966929408.0000
Epoch [7320/10000], Loss: 18900688896.0000
Epoch [7330/10000], Loss: 18963290112.0000
Epoch [7340/10000], Loss: 18881114112.0000
Epoch [7350/10000], Loss: 18833551360.0000
Epoch [7360/10000], Loss: 18858723328.0000
Epoch [7370/10000], Loss: 18847524864.0000
Epoch [7380/10000], Loss: 18845157376.0000
Epoch [7390/10000], Loss: 18783512576.0000
Epoch [7400/10000], Loss: 18763503616.0000
Epoch [7410/10000], Loss: 18774749184.0000
Epoch [7420/10000], Loss: 18684508160.0000
Epoch [7430/10000], Loss: 18706425856.0000
Epoch [7440/10000], Loss: 18726985728.0000
Epoch [7450/10000], Loss: 18660884480.0000
Epoch [7460/10000], Loss: 18609545216.0000
Epoch [7470/10000], Loss: 18663884800.0000
Epoch [7480/10000], Loss: 18644033536.0000
Epoch [7490/10000], Loss: 18574614528.0000
Epoch [7500/10000], Loss: 18532057088.0000
Epoch [7510/10000], Loss: 18593206272.0000
Epoch [7520/10000], Loss: 18500268032.0000
Epoch [7530/10000], Loss: 18471202816.0000
Epoch [7540/10000], Loss: 18492487680.0000
Epoch [7550/10000], Loss: 18522370048.0000
Epoch [7560/10000], Loss: 18427199488.0000
Epoch [7570/10000], Loss: 18377887744.0000
Epoch [7580/10000], Loss: 18439983104.0000
Epoch [7590/10000], Loss: 18399823872.0000
Epoch [7600/10000], Loss: 18316818432.0000
Epoch [7610/10000], Loss: 18327015424.0000
Epoch [7620/10000], Loss: 18376284160.0000
Epoch [7630/10000], Loss: 18315929600.0000
Epoch [7640/10000], Loss: 18237655040.0000
Epoch [7650/10000], Loss: 18266791936.0000
Epoch [7660/10000], Loss: 18320185344.0000
Epoch [7670/10000], Loss: 18258964480.0000
Epoch [7680/10000], Loss: 18192740352.0000
Epoch [7690/10000], Loss: 18158239744.0000
Epoch [7700/10000], Loss: 18181695488.0000
Epoch [7710/10000], Loss: 18181761024.0000
Epoch [7720/10000], Loss: 18080423936.0000
Epoch [7730/10000], Loss: 18158579712.0000
Epoch [7740/10000], Loss: 18100051968.0000
Epoch [7750/10000], Loss: 18035136512.0000
Epoch [7760/10000], Loss: 18030438400.0000
Epoch [7770/10000], Loss: 18113280000.0000
Epoch [7780/10000], Loss: 18066647040.0000
Epoch [7790/10000], Loss: 18000637952.0000
Epoch [7800/10000], Loss: 17934180352.0000
Epoch [7810/10000], Loss: 18031814656.0000
Epoch [7820/10000], Loss: 17979893760.0000
Epoch [7830/10000], Loss: 17896286208.0000
Epoch [7840/10000], Loss: 17868812288.0000
Epoch [7850/10000], Loss: 17932695552.0000
Epoch [7860/10000], Loss: 17895813120.0000
Epoch [7870/10000], Loss: 17815005184.0000
Epoch [7880/10000], Loss: 17793396736.0000
Epoch [7890/10000], Loss: 17829416960.0000
Epoch [7900/10000], Loss: 17849985024.0000
Epoch [7910/10000], Loss: 17754613760.0000
Epoch [7920/10000], Loss: 17734287360.0000
Epoch [7930/10000], Loss: 17839464448.0000
Epoch [7940/10000], Loss: 17740017664.0000
Epoch [7950/10000], Loss: 17682644992.0000
Epoch [7960/10000], Loss: 17683396608.0000
Epoch [7970/10000], Loss: 17688461312.0000
Epoch [7980/10000], Loss: 17609519104.0000
Epoch [7990/10000], Loss: 17693644800.0000
Epoch [8000/10000], Loss: 17677570048.0000
Epoch [8010/10000], Loss: 17598138368.0000
Epoch [8020/10000], Loss: 17534072832.0000
Epoch [8030/10000], Loss: 17622708224.0000
Epoch [8040/10000], Loss: 17575421952.0000
Epoch [8050/10000], Loss: 17504624640.0000
Epoch [8060/10000], Loss: 17505593344.0000
Epoch [8070/10000], Loss: 17522780160.0000
Epoch [8080/10000], Loss: 17429184512.0000
Epoch [8090/10000], Loss: 17424078848.0000
Epoch [8100/10000], Loss: 17506009088.0000
Epoch [8110/10000], Loss: 17373241344.0000
Epoch [8120/10000], Loss: 17429035008.0000
Epoch [8130/10000], Loss: 17420546048.0000
Epoch [8140/10000], Loss: 17318205440.0000
Epoch [8150/10000], Loss: 17383505920.0000
Epoch [8160/10000], Loss: 17356316672.0000
Epoch [8170/10000], Loss: 17270130688.0000
Epoch [8180/10000], Loss: 17338884096.0000
Epoch [8190/10000], Loss: 17327214592.0000
Epoch [8200/10000], Loss: 17279463424.0000
Epoch [8210/10000], Loss: 17206632448.0000
Epoch [8220/10000], Loss: 17262927872.0000
Epoch [8230/10000], Loss: 17165797376.0000
Epoch [8240/10000], Loss: 17193099264.0000
Epoch [8250/10000], Loss: 17237813248.0000
Epoch [8260/10000], Loss: 17145164800.0000
Epoch [8270/10000], Loss: 17097780224.0000
Epoch [8280/10000], Loss: 17155079168.0000
Epoch [8290/10000], Loss: 17132945408.0000
Epoch [8300/10000], Loss: 17036755968.0000
Epoch [8310/10000], Loss: 17115918336.0000
Epoch [8320/10000], Loss: 17099726848.0000
Epoch [8330/10000], Loss: 17004758016.0000
Epoch [8340/10000], Loss: 16995623936.0000
Epoch [8350/10000], Loss: 17025379328.0000
Epoch [8360/10000], Loss: 17042255872.0000
Epoch [8370/10000], Loss: 16924684288.0000
Epoch [8380/10000], Loss: 16950146048.0000
Epoch [8390/10000], Loss: 17024071680.0000
Epoch [8400/10000], Loss: 16905218048.0000
Epoch [8410/10000], Loss: 16875421696.0000
Epoch [8420/10000], Loss: 16981414912.0000
Epoch [8430/10000], Loss: 16891516928.0000
Epoch [8440/10000], Loss: 16805768192.0000
Epoch [8450/10000], Loss: 16848031744.0000
Epoch [8460/10000], Loss: 16894860288.0000
Epoch [8470/10000], Loss: 16858976256.0000
Epoch [8480/10000], Loss: 16758952960.0000
Epoch [8490/10000], Loss: 16730971136.0000
Epoch [8500/10000], Loss: 16786704384.0000
Epoch [8510/10000], Loss: 16795893760.0000
Epoch [8520/10000], Loss: 16683686912.0000
Epoch [8530/10000], Loss: 16702092288.0000
Epoch [8540/10000], Loss: 16763962368.0000
Epoch [8550/10000], Loss: 16683048960.0000
Epoch [8560/10000], Loss: 16645136384.0000
Epoch [8570/10000], Loss: 16750691328.0000
Epoch [8580/10000], Loss: 16652770304.0000
Epoch [8590/10000], Loss: 16577718272.0000
Epoch [8600/10000], Loss: 16621923328.0000
Epoch [8610/10000], Loss: 16633239552.0000
Epoch [8620/10000], Loss: 16540891136.0000
Epoch [8630/10000], Loss: 16578623488.0000
Epoch [8640/10000], Loss: 16655206400.0000
Epoch [8650/10000], Loss: 16556931072.0000
Epoch [8660/10000], Loss: 16465077248.0000
Epoch [8670/10000], Loss: 16632245248.0000
Epoch [8680/10000], Loss: 16548523008.0000
Epoch [8690/10000], Loss: 16435972096.0000
Epoch [8700/10000], Loss: 16439989248.0000
Epoch [8710/10000], Loss: 16502726656.0000
Epoch [8720/10000], Loss: 16405437440.0000
Epoch [8730/10000], Loss: 16448935936.0000
Epoch [8740/10000], Loss: 16401833984.0000
Epoch [8750/10000], Loss: 16334807040.0000
Epoch [8760/10000], Loss: 16368381952.0000
Epoch [8770/10000], Loss: 16473218048.0000
Epoch [8780/10000], Loss: 16374027264.0000
Epoch [8790/10000], Loss: 16282378240.0000
Epoch [8800/10000], Loss: 16293383168.0000
Epoch [8810/10000], Loss: 16429017088.0000
Epoch [8820/10000], Loss: 16278544384.0000
Epoch [8830/10000], Loss: 16236674048.0000
Epoch [8840/10000], Loss: 16370742272.0000
Epoch [8850/10000], Loss: 16204353536.0000
Epoch [8860/10000], Loss: 16282034176.0000
Epoch [8870/10000], Loss: 16165651456.0000
Epoch [8880/10000], Loss: 16345307136.0000
Epoch [8890/10000], Loss: 16181713920.0000
Epoch [8900/10000], Loss: 16144455680.0000
Epoch [8910/10000], Loss: 16199261184.0000
Epoch [8920/10000], Loss: 16226864128.0000
Epoch [8930/10000], Loss: 16151510016.0000
Epoch [8940/10000], Loss: 16069234688.0000
Epoch [8950/10000], Loss: 16221099008.0000
Epoch [8960/10000], Loss: 16138140672.0000
Epoch [8970/10000], Loss: 16031289344.0000
Epoch [8980/10000], Loss: 16076396544.0000
Epoch [8990/10000], Loss: 16126156800.0000
Epoch [9000/10000], Loss: 16023005184.0000
Epoch [9010/10000], Loss: 16007345152.0000
Epoch [9020/10000], Loss: 16181982208.0000
Epoch [9030/10000], Loss: 16084757504.0000
Epoch [9040/10000], Loss: 16001732608.0000
Epoch [9050/10000], Loss: 15942478848.0000
Epoch [9060/10000], Loss: 15951780864.0000
Epoch [9070/10000], Loss: 15944798208.0000
Epoch [9080/10000], Loss: 15896205312.0000
Epoch [9090/10000], Loss: 15997906944.0000
Epoch [9100/10000], Loss: 15852871680.0000
Epoch [9110/10000], Loss: 15904259072.0000
Epoch [9120/10000], Loss: 15992311808.0000
Epoch [9130/10000], Loss: 15935448064.0000
Epoch [9140/10000], Loss: 15839268864.0000
Epoch [9150/10000], Loss: 15783864320.0000
Epoch [9160/10000], Loss: 15958487040.0000
Epoch [9170/10000], Loss: 15860670464.0000
Epoch [9180/10000], Loss: 15783315456.0000
Epoch [9190/10000], Loss: 15734597632.0000
Epoch [9200/10000], Loss: 15819678720.0000
Epoch [9210/10000], Loss: 15746620416.0000
Epoch [9220/10000], Loss: 15719409664.0000
Epoch [9230/10000], Loss: 15812999168.0000
Epoch [9240/10000], Loss: 15751278592.0000
Epoch [9250/10000], Loss: 15644568576.0000
Epoch [9260/10000], Loss: 15733157888.0000
Epoch [9270/10000], Loss: 15724306432.0000
Epoch [9280/10000], Loss: 15617004544.0000
Epoch [9290/10000], Loss: 15731532800.0000
Epoch [9300/10000], Loss: 15657965568.0000
Epoch [9310/10000], Loss: 15581690880.0000
Epoch [9320/10000], Loss: 15687882752.0000
Epoch [9330/10000], Loss: 15626765312.0000
Epoch [9340/10000], Loss: 15538247680.0000
Epoch [9350/10000], Loss: 15622797312.0000
Epoch [9360/10000], Loss: 15562005504.0000
Epoch [9370/10000], Loss: 15501617152.0000
Epoch [9380/10000], Loss: 15615681536.0000
Epoch [9390/10000], Loss: 15465839616.0000
Epoch [9400/10000], Loss: 15553048576.0000
Epoch [9410/10000], Loss: 15618019328.0000
Epoch [9420/10000], Loss: 15516972032.0000
Epoch [9430/10000], Loss: 15428354048.0000
Epoch [9440/10000], Loss: 15442135040.0000
Epoch [9450/10000], Loss: 15511350272.0000
Epoch [9460/10000], Loss: 15395200000.0000
Epoch [9470/10000], Loss: 15453029376.0000
Epoch [9480/10000], Loss: 15446786048.0000
Epoch [9490/10000], Loss: 15334318080.0000
Epoch [9500/10000], Loss: 15462760448.0000
Epoch [9510/10000], Loss: 15401046016.0000
Epoch [9520/10000], Loss: 15304540160.0000
Epoch [9530/10000], Loss: 15434773504.0000
Epoch [9540/10000], Loss: 15322351616.0000
Epoch [9550/10000], Loss: 15294841856.0000
Epoch [9560/10000], Loss: 15414770688.0000
Epoch [9570/10000], Loss: 15289768960.0000
Epoch [9580/10000], Loss: 15251183616.0000
Epoch [9590/10000], Loss: 15320202240.0000
Epoch [9600/10000], Loss: 15263808512.0000
Epoch [9610/10000], Loss: 15206699008.0000
Epoch [9620/10000], Loss: 15306333184.0000
Epoch [9630/10000], Loss: 15167053824.0000
Epoch [9640/10000], Loss: 15350538240.0000
Epoch [9650/10000], Loss: 15247004672.0000
Epoch [9660/10000], Loss: 15136593920.0000
Epoch [9670/10000], Loss: 15176950784.0000
Epoch [9680/10000], Loss: 15243730944.0000
Epoch [9690/10000], Loss: 15197547520.0000
Epoch [9700/10000], Loss: 15077328896.0000
Epoch [9710/10000], Loss: 15165659136.0000
Epoch [9720/10000], Loss: 15182614528.0000
Epoch [9730/10000], Loss: 15040357376.0000
Epoch [9740/10000], Loss: 15124745216.0000
Epoch [9750/10000], Loss: 15145594880.0000
Epoch [9760/10000], Loss: 15004985344.0000
Epoch [9770/10000], Loss: 15112262656.0000
Epoch [9780/10000], Loss: 15043771392.0000
Epoch [9790/10000], Loss: 15015484416.0000
Epoch [9800/10000], Loss: 15173045248.0000
Epoch [9810/10000], Loss: 15010365440.0000
Epoch [9820/10000], Loss: 14946551808.0000
Epoch [9830/10000], Loss: 15003002880.0000
Epoch [9840/10000], Loss: 15035953152.0000
Epoch [9850/10000], Loss: 14918465536.0000
Epoch [9860/10000], Loss: 14948540416.0000
Epoch [9870/10000], Loss: 14975939584.0000
Epoch [9880/10000], Loss: 14882556928.0000
Epoch [9890/10000], Loss: 14960998400.0000
Epoch [9900/10000], Loss: 14955343872.0000
Epoch [9910/10000], Loss: 14840415232.0000
Epoch [9920/10000], Loss: 14979625984.0000
Epoch [9930/10000], Loss: 14938395648.0000
Epoch [9940/10000], Loss: 14787784704.0000
Epoch [9950/10000], Loss: 14946582528.0000
Epoch [9960/10000], Loss: 14896962560.0000
Epoch [9970/10000], Loss: 14750309376.0000
Epoch [9980/10000], Loss: 14930928640.0000
Epoch [9990/10000], Loss: 14782153728.0000
Epoch [10000/10000], Loss: 14777547776.0000
Test Loss: 57464336384.0000
Training time: 23.42 seconds

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	lat	long	sqft_living15	sqft_lot15	house_age	years_since_renovation	num_rooms	living percentage	price_per_sqft_living	price_per_sqft_lot
id	8.274654e+18	1.566785e+23	-1.770230e+13	3.316999e+06	1.142695e+07	-3.238863e+10	-1.574064e+13	2.877320e+07	-6.772400e+05	2.555269e+07	...	-7.539282e+05	8.425348e+06	-5.722532e+09	-1.090153e+13	-1.792808e+09	-1.434415e+09	1.474395e+07	6.806534e+07	-1.749445e+09	1.618107e+10
date	1.566785e+23	9.540109e+31	-1.562305e+19	-1.525486e+14	-2.588457e+14	-3.100206e+17	2.553979e+18	-1.186211e+14	1.145477e+12	-1.347551e+13	...	-4.446697e+13	-9.655969e+12	-2.109778e+17	6.842317e+17	3.999961e+15	7.098913e+15	-4.113943e+14	-1.267093e+13	4.081743e+16	7.885853e+15
price	-1.770230e+13	-1.562305e+19	1.347821e+11	1.053004e+05	1.484810e+05	2.367150e+08	1.363432e+09	5.090779e+04	8.460640e+03	1.117729e+05	...	1.561740e+04	1.118099e+03	1.472961e+08	8.264560e+08	-5.818276e+05	-1.117816e+06	2.537814e+05	1.213879e+04	2.241334e+07	1.063007e+07
bedrooms	3.316999e+06	-1.525486e+14	1.053004e+05	8.650956e-01	3.695787e-01	4.926377e+02	1.221762e+03	8.812733e-02	-5.293190e-04	5.669500e-02	...	-1.148704e-03	1.696024e-02	2.496817e+02	7.429740e+02	-4.217009e+00	-4.439367e+00	1.234674e+00	6.658814e-03	-2.109089e+01	-5.451275e+00
bathrooms	1.142695e+07	-2.588457e+14	1.484810e+05	3.695787e-01	5.931513e-01	5.338120e+02	2.798944e+03	2.082114e-01	4.247388e-03	1.108004e-01	...	2.622344e-03	2.419130e-02	3.001611e+02	1.833182e+03	-1.145691e+01	-1.192709e+01	9.627300e-01	5.922581e-02	-7.749677e+00	1.464115e+01
sqft_living	-3.238863e+10	-3.100206e+17	2.367150e+08	4.926377e+02	5.338120e+02	8.435337e+05	6.574684e+06	1.755404e+02	8.249461e+00	2.003143e+02	...	6.685035e+00	3.107108e+01	4.761601e+05	4.596302e+06	-8.592709e+03	-9.107057e+03	1.026450e+03	1.904793e+01	-9.331851e+03	1.947152e+03
sqft_lot	-1.574064e+13	2.553979e+18	1.363432e+09	1.221762e+03	2.798944e+03	6.574684e+06	1.715659e+09	-1.163286e+02	7.741867e+01	2.371393e+03	...	-4.917661e+02	1.338837e+03	4.105319e+06	8.126540e+08	-6.447493e+04	-6.302673e+04	4.020705e+03	-2.811031e+03	-1.541897e+05	-8.039670e+05
floors	2.877320e+07	-1.186211e+14	5.090779e+04	8.812733e-02	2.082114e-01	1.755404e+02	-1.163286e+02	2.915880e-01	1.107146e-03	1.218394e-02	...	3.712271e-03	9.537583e-03	1.035866e+02	-1.661524e+02	-7.766885e+00	-7.867447e+00	2.963387e-01	8.078183e-02	2.280253e-01	2.428315e+01
waterfront	-6.772400e+05	1.145477e+12	8.460640e+03	-5.293190e-04	4.247388e-03	8.249461e+00	7.741867e+01	1.107146e-03	7.485226e-03	2.664300e-02	...	-1.711161e-04	-5.106370e-04	5.127103e+00	7.252979e+01	6.631481e-02	1.169693e-03	3.718069e-03	-6.941712e-04	1.839817e+00	2.490478e-01
view	2.555269e+07	-1.347551e+13	1.117729e+05	5.669500e-02	1.108004e-01	2.003143e+02	2.371393e+03	1.218394e-02	2.664300e-02	5.872426e-01	...	6.537452e-04	-8.460837e-03	1.472943e+02	1.518526e+03	1.203386e+00	4.036811e-01	1.674954e-01	-2.690266e-04	1.863367e+01	5.681183e+00
condition	-4.452067e+07	-3.226869e+14	8.686852e+03	1.725115e-02	-6.263824e-02	-3.511460e+01	-2.414616e+02	-9.268648e-02	9.375805e-04	2.293397e-02	...	-1.347221e-03	-9.760027e-03	-4.140089e+01	-6.050935e+01	6.894440e+00	7.416206e+00	-4.538708e-02	-2.727252e-02	7.329965e+00	-5.252303e+00
grade	2.748834e+07	-4.582380e+14	2.880262e+05	3.902840e-01	6.020054e-01	8.234077e+02	5.531997e+03	2.908243e-01	8.417993e-03	2.263832e-01	...	1.858155e-02	3.283811e-02	5.745907e+02	3.827254e+03	-1.544911e+01	-1.561983e+01	9.922894e-01	6.053961e-02	1.588124e+01	2.412967e+01
sqft_above	-2.582941e+10	-2.258601e+17	1.841011e+08	3.678642e+02	4.370876e+02	6.666978e+05	6.294462e+06	2.342603e+02	5.163720e+00	1.063870e+02	...	-9.368779e-02	4.009385e+01	4.153850e+05	4.387534e+06	-1.032007e+04	-1.040758e+04	8.049518e+02	1.154410e+01	-8.076881e+03	-3.563238e+02
sqft_basement	-6.559226e+09	-8.416047e+16	5.261393e+07	1.247734e+02	9.672443e+01	1.768358e+05	2.802218e+05	-5.871985e+01	3.085741e+00	9.392727e+01	...	6.778723e+00	-9.022770e+00	6.077510e+04	2.087679e+05	1.727359e+03	1.300528e+03	2.214979e+02	7.503838e+00	-1.254971e+03	2.303476e+03
yr_built	1.806430e+09	-1.019331e+14	5.824414e+05	4.212745e+00	1.144733e+01	8.580238e+03	6.458085e+04	7.761250e+00	-6.648330e-02	-1.202897e+00	...	-6.028713e-01	1.693346e+00	6.567732e+03	5.690946e+04	-8.627491e+02	-7.698889e+02	1.566008e+01	2.202409e+00	-9.371025e+02	3.442951e+02
yr_renovated	-1.953565e+10	-9.615773e+16	1.864482e+07	7.042583e+00	1.569654e+01	2.042442e+04	1.271708e+05	1.374814e+00	3.227949e+00	3.198718e+01	...	1.636217e+00	-3.867676e+00	-7.357744e+02	8.613634e+04	2.648760e+03	-1.908793e+03	2.273912e+01	-3.664272e-01	4.663262e+03	1.399137e+03
zipcode	-1.265349e+09	7.335453e+14	-1.045028e+06	-7.601869e+00	-8.400840e+00	-9.800232e+03	-2.871637e+05	-1.708121e+00	1.401912e-01	3.478060e+00	...	1.979855e+00	-4.250293e+00	-1.023266e+04	-2.150769e+05	5.451781e+02	4.942046e+02	-1.600271e+01	2.555770e+00	1.016621e+03	1.115758e+03
lat	-7.539282e+05	-4.446697e+13	1.561740e+04	-1.148704e-03	2.622344e-03	6.685035e+00	-4.917661e+02	3.712271e-03	-1.711161e-04	6.537452e-04	...	1.919990e-02	-2.644336e-03	4.640056e+00	-3.269542e+02	6.009785e-01	5.391607e-01	1.473640e-03	6.138167e-03	7.198930e+00	3.791880e+00
long	8.425348e+06	-9.655969e+12	1.118099e+03	1.696024e-02	2.419130e-02	3.107108e+01	1.338837e+03	9.537583e-03	-5.106370e-04	-8.460837e-03	...	-2.644336e-03	1.983262e-02	3.229692e+01	9.784167e+02	-1.693329e+00	-1.553609e+00	4.115154e-02	-7.729806e-03	-3.658430e+00	-3.569414e+00
sqft_living15	-5.722532e+09	-2.109778e+17	1.472961e+08	2.496817e+02	3.001611e+02	4.761601e+05	4.105319e+06	1.035866e+02	5.127103e+00	1.472943e+02	...	4.640056e+00	3.229692e+01	4.697612e+05	3.428259e+06	-6.574697e+03	-6.415450e+03	5.498428e+02	-7.755340e+00	2.908721e+03	-2.867380e+03
sqft_lot15	-1.090153e+13	6.842317e+17	8.264560e+08	7.429740e+02	1.833182e+03	4.596302e+06	8.126540e+08	-1.661524e+02	7.252979e+01	1.518526e+03	...	-3.269542e+02	9.784167e+02	3.428259e+06	7.455182e+08	-5.691054e+04	-5.523156e+04	2.576156e+03	-2.034166e+03	-1.728430e+05	-5.873583e+05
house_age	-1.792808e+09	3.999961e+15	-5.818276e+05	-4.217009e+00	-1.145691e+01	-8.592709e+03	-6.447493e+04	-7.766885e+00	6.631481e-02	1.203386e+00	...	6.009785e-01	-1.693329e+00	-6.574697e+03	-5.691054e+04	8.629196e+02	7.701938e+02	-1.567392e+01	-2.203452e+00	9.394453e+02	-3.438461e+02
years_since_renovation	-1.434415e+09	7.098913e+15	-1.117816e+06	-4.439367e+00	-1.192709e+01	-9.107057e+03	-6.302673e+04	-7.867447e+00	1.169693e-03	4.036811e-01	...	5.391607e-01	-1.553609e+00	-6.415450e+03	-5.523156e+04	7.701938e+02	8.302260e+02	-1.636646e+01	-2.294506e+00	7.924248e+02	-4.208357e+02
num_rooms	1.474395e+07	-4.113943e+14	2.537814e+05	1.234674e+00	9.627300e-01	1.026450e+03	4.020705e+03	2.963387e-01	3.718069e-03	1.674954e-01	...	1.473640e-03	4.115154e-02	5.498428e+02	2.576156e+03	-1.567392e+01	-1.636646e+01	2.197404e+00	6.588463e-02	-2.884057e+01	9.189874e+00
living percentage	6.806534e+07	-1.267093e+13	1.213879e+04	6.658814e-03	5.922581e-02	1.904793e+01	-2.811031e+03	8.078183e-02	-6.941712e-04	-2.690266e-04	...	6.138167e-03	-7.729806e-03	-7.755340e+00	-2.034166e+03	-2.203452e+00	-2.294506e+00	6.588463e-02	7.212734e-02	3.447523e+00	2.263822e+01
price_per_sqft_living	-1.749445e+09	4.081743e+16	2.241334e+07	-2.109089e+01	-7.749677e+00	-9.331851e+03	-1.541897e+05	2.280253e-01	1.839817e+00	1.863367e+01	...	7.198930e+00	-3.658430e+00	2.908721e+03	-1.728430e+05	9.394453e+02	7.924248e+02	-2.884057e+01	3.447523e+00	1.211332e+04	4.663091e+03
price_per_sqft_lot	1.618107e+10	7.885853e+15	1.063007e+07	-5.451275e+00	1.464115e+01	1.947152e+03	-8.039670e+05	2.428315e+01	2.490478e-01	5.681183e+00	...	3.791880e+00	-3.569414e+00	-2.867380e+03	-5.873583e+05	-3.438461e+02	-4.208357e+02	9.189874e+00	2.263822e+01	4.663091e+03	8.846382e+03

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	...	lat	long	sqft_living15	sqft_lot15	house_age	years_since_renovation	num_rooms	living percentage	price_per_sqft_living	price_per_sqft_lot
id	1.000000	0.005576	-0.016762	0.001240	0.005158	-0.012259	-0.132109	0.018524	-0.002721	0.011592	...	-0.001891	0.020798	-0.002903	-0.138798	-0.021216	-0.017306	0.003458	0.088105	-0.005526	0.059807
date	0.005576	1.000000	-0.004357	-0.016792	-0.034410	-0.034559	0.006313	-0.022491	0.001356	-0.001800	...	-0.032856	-0.007020	-0.031515	0.002566	0.013941	0.025224	-0.028414	-0.004830	0.037970	0.008584
price	-0.016762	-0.004357	1.000000	0.308377	0.525136	0.702035	0.089661	0.256793	0.266370	0.397294	...	0.307003	0.021626	0.585378	0.082447	-0.053950	-0.105671	0.466325	0.123115	0.554701	0.307848
bedrooms	0.001240	-0.016792	0.308377	1.000000	0.515932	0.576693	0.031713	0.175466	-0.006578	0.079543	...	-0.008913	0.129482	0.391666	0.029256	-0.154343	-0.165650	0.895500	0.026657	-0.206030	-0.062314
bathrooms	0.005158	-0.034410	0.525136	0.515932	1.000000	0.754665	0.087740	0.500653	0.063744	0.187737	...	0.024573	0.223042	0.568634	0.087175	-0.506407	-0.537469	0.843270	0.286338	-0.091426	0.202120
sqft_living	-0.012259	-0.034559	0.702035	0.576693	0.754665	1.000000	0.172826	0.353949	0.103818	0.284611	...	0.052529	0.240223	0.756420	0.183286	-0.318488	-0.344135	0.753931	0.077223	-0.092318	0.022541
sqft_lot	-0.132109	0.006313	0.089661	0.031713	0.087740	0.172826	1.000000	-0.005201	0.021604	0.074710	...	-0.085683	0.229521	0.144608	0.718557	-0.052990	-0.052809	0.065483	-0.252697	-0.033823	-0.206367
floors	0.018524	-0.022491	0.256793	0.175466	0.500653	0.353949	-0.005201	1.000000	0.023698	0.029444	...	0.049614	0.125419	0.279885	-0.011269	-0.489640	-0.505651	0.370210	0.557030	0.003837	0.478120
waterfront	-0.002721	0.001356	0.266370	-0.006578	0.063744	0.103818	0.021604	0.023698	1.000000	0.401857	...	-0.014274	-0.041910	0.086463	0.030703	0.026093	0.000469	0.028991	-0.029875	0.193215	0.030605
view	0.011592	-0.001800	0.397294	0.079543	0.187737	0.284611	0.074710	0.029444	0.401857	1.000000	...	0.006157	-0.078400	0.280439	0.072575	0.053458	0.018282	0.147448	-0.001307	0.220932	0.078822
condition	-0.023784	-0.050769	0.036361	0.028502	-0.124982	-0.058753	-0.008958	-0.263768	0.016653	0.045990	...	-0.014941	-0.106500	-0.092824	-0.003406	0.360665	0.395525	-0.047051	-0.156051	0.102344	-0.085814
grade	0.008130	-0.039912	0.667434	0.356978	0.664983	0.762704	0.113621	0.458183	0.082775	0.251321	...	0.114084	0.198372	0.713202	0.119248	-0.447415	-0.461180	0.569477	0.191771	0.122757	0.218253
sqft_above	-0.010843	-0.027924	0.605567	0.477614	0.685342	0.876597	0.183512	0.523885	0.072075	0.167649	...	-0.000816	0.343803	0.731870	0.194050	-0.424248	-0.436188	0.655748	0.051908	-0.088620	-0.004575
sqft_basement	-0.005152	-0.019469	0.323816	0.303112	0.283770	0.435043	0.015286	-0.245705	0.080588	0.276947	...	0.110538	-0.144765	0.200355	0.017276	0.132865	0.101985	0.337620	0.063132	-0.025764	0.055337
yr_built	0.021379	-0.000355	0.054011	0.154198	0.506019	0.318049	0.053080	0.489319	-0.026161	-0.053440	...	-0.148122	0.409356	0.326229	0.070958	-0.999873	-0.909652	0.359654	0.279186	-0.289869	0.124622
yr_renovated	-0.016907	-0.024509	0.126434	0.018850	0.050739	0.055363	0.007644	0.006338	0.092885	0.103917	...	0.029398	-0.068372	-0.002673	0.007854	0.224480	-0.164923	0.038189	-0.003397	0.105482	0.037034
zipcode	-0.008221	0.001404	-0.053201	-0.152754	-0.203866	-0.199430	-0.129574	-0.059121	0.030285	0.084827	...	0.267048	-0.564072	-0.279033	-0.147221	0.346864	0.320563	-0.201764	0.177860	0.172637	0.221714
lat	-0.001891	-0.032856	0.307003	-0.008913	0.024573	0.052529	-0.085683	0.049614	-0.014274	0.006157	...	1.000000	-0.135512	0.048858	-0.086419	0.147647	0.135043	0.007174	0.164945	0.472049	0.290953
long	0.020798	-0.007020	0.021626	0.129482	0.223042	0.240223	0.229521	0.125419	-0.041910	-0.078400	...	-0.135512	1.000000	0.334605	0.254451	-0.409323	-0.382872	0.197125	-0.204375	-0.236033	-0.269478
sqft_living15	-0.002903	-0.031515	0.585378	0.391666	0.568634	0.756420	0.144608	0.279885	0.086463	0.280439	...	0.048858	0.334605	1.000000	0.183192	-0.326552	-0.324856	0.541184	-0.042132	0.038560	-0.044480
sqft_lot15	-0.138798	0.002566	0.082447	0.029256	0.087175	0.183286	0.718557	-0.011269	0.030703	0.072575	...	-0.086419	0.254451	0.183192	1.000000	-0.070954	-0.070204	0.063648	-0.277401	-0.057516	-0.228713
house_age	-0.021216	0.013941	-0.053950	-0.154343	-0.506407	-0.318488	-0.052990	-0.489640	0.026093	0.053458	...	0.147647	-0.409323	-0.326552	-0.070954	1.000000	0.909948	-0.359946	-0.279298	0.290573	-0.124450
years_since_renovation	-0.017306	0.025224	-0.105671	-0.165650	-0.537469	-0.344135	-0.052809	-0.505651	0.000469	0.018282	...	0.135043	-0.382872	-0.324856	-0.070204	0.909948	1.000000	-0.383179	-0.296511	0.249878	-0.155286
num_rooms	0.003458	-0.028414	0.466325	0.895500	0.843270	0.753931	0.065483	0.370210	0.028991	0.147448	...	0.007174	0.197125	0.541184	0.063648	-0.359946	-0.383179	1.000000	0.165493	-0.176774	0.065913
living percentage	0.088105	-0.004830	0.123115	0.026657	0.286338	0.077223	-0.252697	0.557030	-0.029875	-0.001307	...	0.164945	-0.204375	-0.042132	-0.277401	-0.279298	-0.296511	0.165493	1.000000	0.116634	0.896209
price_per_sqft_living	-0.005526	0.037970	0.554701	-0.206030	-0.091426	-0.092318	-0.033823	0.003837	0.193215	0.220932	...	0.472049	-0.236033	0.038560	-0.057516	0.290573	0.249878	-0.176774	0.116634	1.000000	0.450463
price_per_sqft_lot	0.059807	0.008584	0.307848	-0.062314	0.202120	0.022541	-0.206367	0.478120	0.030605	0.078822	...	0.290953	-0.269478	-0.044480	-0.228713	-0.124450	-0.155286	0.065913	0.896209	0.450463	1.000000

	zipcode	avg_price	avg_price_per_sqft	avg_sqft_living	avg_sqft_lot	avg_age	total_houses
0	98001	2.808047e+05	151.387938	1900.856354	14937.450276	33.643646	362
1	98002	2.342840e+05	151.174091	1627.743719	7517.633166	46.562814	199
2	98003	2.941113e+05	157.113414	1928.882143	10603.096429	37.457143	280
3	98004	1.355927e+06	475.435611	2909.022082	13104.220820	42.867508	317
4	98005	8.101649e+05	314.929231	2656.803571	19928.785714	44.553571	168
...	...	...	...	...	...	...	...
65	98177	6.761854e+05	292.918745	2323.333333	11904.403922	53.447059	255
66	98178	3.106486e+05	189.202933	1729.351145	8309.122137	59.061069	262
67	98188	2.890783e+05	169.007306	1802.772059	10126.080882	48.882353	136
68	98198	3.028789e+05	178.428610	1745.360714	10525.978571	47.614286	280
69	98199	7.918208e+05	376.546345	2161.798107	5436.283912	57.798107	317

	zipcode	avg_price	avg_price_per_sqft	avg_sqft_living	avg_sqft_lot	avg_age	total_houses	house_age
zipcode	1.000000	-0.097518	0.151576	-0.408197	-0.348230	0.622144	0.024878	NaN
avg_price	-0.097518	1.000000	0.865891	0.765753	-0.077480	0.112726	-0.150655	NaN
avg_price_per_sqft	0.151576	0.865891	1.000000	0.397018	-0.214734	0.471106	-0.078179	NaN
avg_sqft_living	-0.408197	0.765753	0.397018	1.000000	0.186817	-0.461699	-0.092070	NaN
avg_sqft_lot	-0.348230	-0.077480	-0.214734	0.186817	1.000000	-0.364846	-0.323261	NaN
avg_age	0.622144	0.112726	0.471106	-0.461699	-0.364846	1.000000	-0.145524	NaN
total_houses	0.024878	-0.150655	-0.078179	-0.092070	-0.323261	-0.145524	1.000000	NaN
house_age	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Insights into the secret world of housing prices - Abhiram, Mihir, Samrit, Suzie¶

Summer 2024 Data Science Project¶

Data Collection¶

Data Processing¶

Exploratory Data Analysis and visualization¶

Continuous Data Analysis¶

Boolean/Categorical Data Analysis¶

Chi-Square Test for Independence Between Categorical Variables¶

T-Test for Difference in House Prices: With vs. Without Basement¶

Zipcode Analysis¶

Seasonality Analysis¶

ML Analysis¶

Conclusion¶

	id	date	price	bedrooms	bathrooms	sqft_living	sqft_lot	floors	...	lat	long	sqft_living15	sqft_lot15	house_age	years_since_renovation	num_rooms	living percentage	price_per_sqft_living	price_per_sqft_lot
0	5561000190	2014-05-02	437500.0	3	2.25	1970	35100	2.0	...	47.4635	-121.991	2340	35100	37	37	5.25	0.056125	222.081218	12.464387
1	472000620	2014-05-02	790000.0	3	2.50	2600	4750	1.0	...	47.6833	-122.400	2380	4750	63	63	5.50	0.547368	303.846154	166.315789
2	1024069009	2014-05-02	675000.0	5	2.50	2820	67518	2.0	...	47.5794	-122.025	2820	48351	35	35	7.50	0.041767	239.361702	9.997334
3	7853361370	2014-05-02	555000.0	4	2.50	3310	6500	2.0	...	47.5150	-121.870	2380	5000	2	2	6.50	0.509231	167.673716	85.384615
4	5056500260	2014-05-02	440000.0	4	2.25	2160	8119	1.0	...	47.5443	-122.177	1850	9000	48	48	6.25	0.266043	203.703704	54.193866
5	3438501320	2014-05-02	295000.0	2	2.50	1630	1368	2.0	...	47.5489	-122.363	1590	2306	5	5	4.50	1.191520	180.981595	215.643275
6	1737320120	2014-05-02	470000.0	5	2.50	2210	9655	1.0	...	47.7698	-122.222	2080	8633	38	38	7.50	0.228897	212.669683	48.679441
7	7197300105	2014-05-02	550000.0	4	2.50	1940	10500	1.0	...	47.6830	-122.114	2200	10500	38	38	6.50	0.184762	283.505155	52.380952
8	1999700045	2014-05-02	313000.0	3	1.50	1340	7912	1.5	...	47.7658	-122.339	1480	7940	59	59	4.50	0.169363	233.582090	39.560162
9	1962200037	2014-05-02	626000.0	3	2.25	1750	1572	2.5	...	47.6498	-122.321	2410	3050	9	9	5.25	1.113232	357.714286	398.218830